ParaGlassMenu: Towards Social-Friendly Subtle Interactions in Conversations

Runze Cai, NUS-HCI Lab, School of Computing, National University of Singapore, Singapore,
Nuwan Nanayakkarawasam Peru Kandage Janaka, NUS-HCI Lab, National University of Singapore, Singapore,
Shengdong Zhao, NUS-HCI Lab, National University of Singapore, Singapore,
Minghui Sun, College of Computer Science and Technology, Jilin University, China,

Interactions with digital devices during social settings can reduce social engagement and interrupt conversations. To overcome these drawbacks, we designed ParaGlassMenu, a semi-transparent circular menu that can be displayed around a conversation partner's face on Optical See-Through Head-Mounted Display (OHMD) and interacted subtly using a ring mouse. We evaluated ParaGlassMenu with several alternative approaches (Smartphone, Voice assistant, and Linear OHMD menus) by manipulating Internet-of-Things (IoT) devices in a simulated conversation setting with a digital partner. Results indicated that the ParaGlassMenu offered the best overall performance in balancing social engagement and digital interaction needs in conversations. To validate these findings, we conducted a second study in a realistic conversation scenario involving commodity IoT devices. Results confirmed the utility and social acceptance of the ParaGlassMenu. Based on the results, we discuss implications for designing attention-maintaining subtle interaction techniques on OHMDs.

CCS Concepts:Human-centered computing → Ubiquitous and mobile computing systems and tools; • Human-centered computing → Empirical studies in interaction design;

Keywords: IoT manipulation, circular menu, ring interaction, social interaction, conversation, HMD, smart glasses, Internet-of-Things

ACM Reference Format:
Runze Cai, Nuwan Nanayakkarawasam Peru Kandage Janaka, Shengdong Zhao, and Minghui Sun. 2023. ParaGlassMenu: Towards Social-Friendly Subtle Interactions in Conversations. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23), April 23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA 21 Pages.

Figure 1
Figure 1: Using ParaGlassMenu (consisting of an Optical See-Through Head-Mounted Display and ring mouse) for digital interactions in social settings. (a) A host is conversing with a guest in the living room while he wants to check the water boiling status in the kitchen using ParaGlassMenu. (b) The host can operate the menu subtly using a ring mouse, which consists of four clickable buttons and one trackpad. (c) A semi-transparent OHMD menu is displayed circularly surrounding the conversation partner's face (located at the same depth as the face). The yellow labels stand for the mappings between icons and buttons. To check the kettle's menu, the host can click the bottom button on the ring mouse.


In an ideal world, face-to-face social interactions are the best when all parties involved give undivided attention to one another. However, real-world situations are often more complex. Considering the following two scenarios: a) John is living alone in his apartment and has decided to host a party in his place. After the arrival of the guests, as the only host, he needs to juggle between the needs of chatting with the guests with the other host duties, including preparing food and drinks, adjusting the environment to make it more comfortable for the guests, etc. b) John is asked to join an ad hoc in-person meeting after work, preventing him from going to a date. His girlfriend, Nicole, unaware of the situation, sends him a message to ask what happened. At this moment, John must choose between ignoring the message, which may upset Nicole [2], or pausing the current conversation to reply to the message, which could impair the face-to-face interaction [19, 40, 56, 78]. Although less desirable, such scenarios are quite common in everyday life as we need to handle multiple requests during social interactions. In such situations, it may be desirable to minimize the interruption of these secondary tasks to the primary social interaction, which leads to the topic of this paper: how to support secondary human-computer interaction with minimal interference to ongoing primary social interactions.

To address this challenge, we propose an interaction technique called ParaGlassMenu, which is designed to support seamless subtle interactions [64] in social settings. ParaGlassMenu incorporates four important design requirements to support general-purpose subtle interactions in social settings.

  • First, it minimizes visual distractions (being non-intrusive [64]) to users during social settings by leveraging the insights of attention-maintaining visualizations [36]. This allows users to focus on their conversational partner while interacting with the menu items displayed in the peripheral area of their vision on an Optical See-Through Head-Mounted Display (OHMD) [34].
  • Second, the input mechanism, using a ring mouse, supports discreet manipulations (hiding activities [64]) cross-scenario [71] to minimize distracting others and protect privacy when necessary [54].
  • Third, ParaGlassMenu supports both discrete and continuous manipulations to accommodate a wider range of interaction needs.
  • Fourth, as a hierarchical menu, ParaGlassMenu is scalable and can accommodate a larger set of commands than many previously proposed subtle interaction techniques (e.g., Jaw-Teeth interaction [8], etc.).

Note that while many other subtle interaction techniques [5, 8, 26, 54, 68] have been proposed, they are missing some of the above design requirements and have not been tested with a solution that combines all of the above features together in social settings (see Related Work, sec 2.2 for details).

When considering the application scenario to evaluate the effectiveness of ParaGlassMenu, we chose Internet-of-Things (IoT) control (e.g., Figure 1c) as it allows us to evaluate ParaGlassMenu with a wider range of interaction types [28, 72], including checking information, discrete and continuous manipulation, and searching. This enables us to better generalize the results of our evaluation to applications with similar interactions (e.g., the example of handling remote social inquiries, Figure 11a). In addition, the use of IoT control applications in social settings enables us to study the manipulation of digital tasks in a social context, where the manipulation can be either personal or for all involved parties [47], and its visibility can be either opaque (discreet and invisible to others) or transparent (noticeable by others) [47, 54]. This provides insights into how users use ParaGlassMenu to manage digital tasks with various purposes in conversations.

We first compare ParaGlassMenu with a Phone Interface, a Voice-User Interface (VUI), and an OHMD linear menu, under a simulated conversation setting in the laboratory. Results showed that ParaGlassMenu was the most preferred interface with the best interaction performance, and the lowest cognitive load and disengagement. To ecologically validate these findings, we further evaluated ParaGlassMenu in a more realistic scenario where users interacted with commodity IoT devices while conversing with a friend in a modeled home. The results confirmed the utility of ParaGlassMenu in a more realistic scenario while revealing additional insights about users’ manipulation behaviors in real-world contexts. Finally, we discuss implications on how to design attention-maintaining subtle interaction techniques with general purpose on OHMDs.

The contributions of this paper thus are threefold: 1) the design of a novel attention-maintaining subtle menu technique called ParaGlassMenu for the emergent OHMD platforms that incorporates four essential design requirements, 2) the empirical validation of the effectiveness of ParaGlassMenu compared to other commonly used approaches in social settings, and 3) insights and design recommendations for creating more effective attention-maintaining subtle interaction design.


Our work is related to the following four areas.

2.1 Digital interactions in social settings

While much work was conducted in this area, we mainly focus on two aspects of it, i.e., categorization and evaluation.

Digital interactions can be categorized in many ways (e.g., interaction modalities), and in social settings, a way to classify them based on their relevance to the people involved in social engagement. From one user's point of view, the digital interactions can either be 1) related to other parties or the common conversation topics, or 2) personal or non-conversation-related [47]. For example, from a host's perspective in a gathering, playing music for everyone can be classified as the former type of digital interaction. While checking email to see whether a message has arrived from her boss (who is not in the gathering) or checking the oven's status in the kitchen can be classified as the latter. Due to the different nature and requirements of digital interactions, their corresponding interaction technique may require a specific design. Literature has suggested they can be treated either transparently or in a hiding manner [47, 54]. Yet how to incorporate these principles of visibility selection into a specific application context is still the job of researchers/designers.

Given that digital interactions can bring potential negative effects on social activities [19, 40, 56, 78], their impacts on conversation quality need to be evaluated in order to determine their feasibility in conversations. Particularly, their abilities to maintain eye contact [6, 7, 31] and minimize impoliteness behaviours [40, 51] should be assessed as they affect users’ active engagement in conversation. Besides, we need to evaluate the social acceptability of interactions from both the users’ and the observers’ perspectives [3, 40, 77], as previous studies have demonstrated positive [58, 75, 81] and negative [33, 41, 78] effects of digital interactions on all involved parties.

2.2 Subtle interactions in social settings

The concept of subtlety has been leveraged to minimize interference in social settings while interacting with digital devices [54, 64]. Based on Pohl's categorization [64], they can be categorized into four types, including 1) being non-intrusive to users’ perception, 2) hiding activity from others, 3) doing less while interacting, and 4) nudging users. In conversation settings, being non-intrusive and hiding activities are the most relevant as they can retain eye contact and politeness for the following reasons. Firstly, non-intrusive interactions can reduce distraction [64], thus helping users maintain attention on others. Secondly, hiding activity in conversation can reduce disruption and gain social acceptance [20, 64], especially when users deal with conversation-irrelevant information.

To support non-intrusive interaction in social settings, several subtle interactions are introduced. For example, HiddenHaptics [54] allows users to receive information through vibro-tactile cues on a smartphone without directly looking at it. However, its vibration feedback only supports relatively simple information [54]. On the other hand, attention-maintaining interfaces [36, 69] utilize the eye's peripheral vision to deliver visual feedback on OHMD and help users maintain attention on the central target [36]. However, such interfaces mainly focus on providing system feedback (output, i.e., uni-directional interaction) to users without allowing user input.

Similarly, many subtle interaction mechanisms have been proposed to hide activities while providing inputs in social settings. These include hand gestural interactions [32], foot plantar-based interactions [26], silent-speech interactions [46], and gaze interactions [68]. Additionally, embedding interactions into common objects allow users to hide their activities, which include mug interaction [12], watch interaction [54], and book interactions [5]. While all of them are useful for different scenarios with different capabilities, they cannot completely replace the usability (e.g., accuracy) of touch mechanisms or mechanical sensors [32, 46]. Besides, only supporting a limited number of discrete commands (e.g., jaw-teeth interaction [8]) makes it less suitable as a general and scalable technique. In addition, some of the above-mentioned subtle techniques are less suitable for conversations (e.g., gaze interactions make it hard to maintain eye contact with conversation partners [68]; jaw-teeth interaction [8] and silent-speech interaction [46] are hard to interact with while speaking).

In contrast, thumb-index interactions using a ring mouse [67, 71], supported by mechanical sensors or touch interactions, strike a balance among usability, convenience, and social acceptability [3]. Their tiny shape makes them easier to carry than other hand-held devices, and the one-hand manipulation nature is more flexible than interactions involving two hands (e.g., watch interaction [54]).

2.3 IoT control interfaces for social settings

We applied the ParaGlassMenu for IoT device manipulation during conversations to evaluate how ParaGlassMenu supports varying complexities and purposes of digital interactions in realistic social settings. Thus, we reviewed existing IoT control interfaces.

Today, users can easily manipulate IoT devices using touch screens on smartphones or dedicated wall-mounted displays [50]. In addition, voice assistants, like Alexa, help users control smart devices [48, 65] in an eyes-free and hands-free manner [66]. Recent research on gesture control interfaces, such as SeleCon [4] and Physical Loci [63], allow users to make intuitive hand gestures to manipulate IoT devices. Social robot [50] also enables users to manipulate IoT devices with tangible icons and expressive gestures, which provides high situational awareness.

Furthermore, the emerging OHMDs allow users to interact with Augmented Reality (AR) menus to control the IoT devices, and several input mechanisms are used alone or together, including gaze, voice, and mid-air gestures [42, 74]. The leverage of OHMDs supports quickly acquiring information in a non-intrusive manner [36, 49] and their different input modalities provide flexible options across different scenarios.

2.3.1 Categorization of existing interfaces. To evaluate the aforementioned interfaces in social settings, Table 1 categorizes them in terms of two dimensions, i.e., attention maintenance (non-intrusive) and manipulation visibility (hiding activities) based on previous frameworks on subtle interactions [5, 64]. We found that because they were designed for different scenarios, supporting eye contact or opaque interaction (i.e., discreet and invisible to others) was not integrated into these interfaces (e.g., gaze interaction can cause attention shift, and voice interaction can make the manipulation transparent). Thus, there is a need for new interfaces to fill this gap. Based on the analysis of subtle interaction in social settings (sec 2.2), we chose a ring mouse supporting thumb-index interaction [67, 71] as the discreet input mechanism. In addition, an OHMD menu placed in the peripheral vision area, thus supporting non-intrusive AR interface [21, 36, 52, 60] was chosen as the output mechanism.

Table 1: IoT control interfaces in conversations can be evaluated in terms of two dimensions, i.e., attention maintenance and manipulation visibility. Attention maintenance has two levels: enable eye contact or not. Manipulation visibility has two levels: transparent or opaque, depending on whether the manipulation is visible. In addition, transparent manipulation has two sub-levels, i.e., fully transparent and semi-transparent, depending on whether the manipulated digital content is known to others. Besides, indicates the interface can support more transparent manipulation naturally by verbally expressing it to conversation partners.
Transparent Manipulation Opaque Manipulation
Fully transparent Semi-transparent
Enable Eye Contact
ParaGlassMenu ParaGlassMenu
Not Enable Eye Contact
Phone, Wall-Mounted Display,
Social Robot, Gaze, Mid-Air Gesture
Phone, Wall-Mounted Display,
Gaze, Mid-Air Gesture

Weigel et al. [80] introduced a flexible input device that can be deformed into various shapes, including a ring. They demonstrated an example of a pie menu on Google Glass and Oculus Rift as one instance of their design space. However, their focus was on the flexible input mechanism rather than interactions in social settings, so they did not provide menu design guidelines or further evaluate their design in social settings. ParaGlassMenu fills this gap by introducing a concrete design that satisfies the four requirements mentioned in the introduction and provides empirical validation. In particular, the requirement of displaying the menu non-intrusively around the face of a conversation partner is not mentioned by Weigel et al., but we believe it is a key insight that contributes to the effectiveness of ParaGlassMenu in supporting seamless digital interactions during social settings.

2.4 Visual Menu design

Menu is a common interaction technique in modern GUI to explore and execute commands. Despite extensive studies (see [10] for a comprehensive review) investigating how menus should be designed to improve productivity in various contexts [10], less is known about their suitability for social contexts. While some menus designed for multitasking usage (e.g., [83]) could potentially be used in social settings, they have not been evaluated under such contexts. Hence, we look into more social-friendly menu designs that allow interactions without affecting social engagement.

To support better social engagement, one approach is to maximize eye contact during a social engagement. Previous studies verified the advantages of presenting the information in the eye's peripheral region to help users keep attention on conversation partners [36, 68].

In seeking a suitable design, we investigated the various layouts presented in literature [10, 79] and identified four possible categories, including: vertical, horizontal, circular, and rectangular. Among them, a vertically arranged, left-aligned linear menu was the most preferred layout for presenting menus [79]. Nonetheless, the circular presentation has been proven to facilitate eye contact in a realistic conversation setting [36], while a linear layout has been found to encourage attention switching between the side visualization and the conversation partner [36]. The difference in previous studies motivates us to further investigate the most suitable layout for non-intrusive bi-directional (considering both input and output) interaction in social settings.

3 ParaGlassMenu

The design inspiration for ParaGlassMenu comes from several branches of prior works. One branch of work is extensive studies on radial style menus, including the Pie Menu [16], Marking Menu [44], Wavelet Menu [25], Flower Menu [9], etc. These menus take advantage of the radial layout, and create menus that are compact and efficient. The second branch of inspiration comes from the recent investigation on OHMD interface design. Given the unique features of OHMD, such as a transparent display that can overlay virtual content on realistic objects, we need to customize the menu designs on OHMD. One particular piece of work that inspired this design is Janaka et al.’s work on the attention-maintaining interface [36], where a circular progress bar is displayed in the para-central and near peripheral vision on the OHMD to enable users to receive digital information while engaged in a social conversation.

We extend the idea of para-central and near peripheral visualization, which only supports uni-directional notification, into an attention-maintaining interaction technique that supports bi-directional information exchange (involving both input and output).

3.1 ParaGlassMenu Design

Figure 2 shows the overall visualization of ParaGlassMenu. The basic design of the menu follows a hierarchical radial menu with all menu items layout radially around the center, which can facilitate visual search for hierarchical menus [70]. However, it has a number of features that are different from a traditional radial menu.

Figure 2
Figure 2: Three instances of menu layouts in ParaGlassMenu demonstrating three usage scenarios. The first panel (rooms) consists of 4 icons corresponding to 4 clickable buttons on the ring mouse. The second panel (AC menu) consists of icons and a circular progress bar. As an indicator of continuous manipulation, the circular progress can be controlled by scrolling the ring mouse's trackpad. The third panel shows a (music) list, and the circular bar indicates that users can also scroll the trackpad to select items.

Position and layout: We designed non-intrusive menus for ParaGlassMenu based on the guideline of attention-maintaining visualization proposed by Janaka et al. [36]. Its menu items circularly surround the target (i.e., the conversation partner's face, Figure 1c) and enable users to check the menu using peripheral vision (angle = 13.7° when focusing on the face center) to minimize the attention switching and any occlusion to the conversation.

Item presentation: The menu items include both icons and text, as icons are easy to recognize, and text provides precise information [76].

Color and transparency: The menu items are rendered in green color following recommendations from prior studies [18, 22] to ensure easy recognition in OHMD. For each menu item, icons are rendered in a semi-transparent fashion to minimize occlusion. For the same reason, items are presented without any connection lines.

Supporting continuous feedback: Circular progress bars (e.g., Figure 2, AC) are adopted as indicators for continuous manipulation (e.g., increasing temperature) and selecting from list, as the circular presentation in peripheral vision could provide non-intrusive feedback at a glance [36].

Ring mouse interaction:

1) Activation: The menu is inactive and not visible on the OHMD by default to minimize unwanted attention during conversations. Hence, users can activate the menus by clicking any of the four buttons on the ring mouse, and consequently, menu items will be shown.

2) Manipulation: The ring mouse supported both clicking and scrolling interaction and followed natural spatial mapping guidelines proposed by Norman [59] to reduce cognitive load. Specifically, as shown in Figure 1b and Figure 1c, users can click the respective button on the ring mouse for item selection, and the selected item will be highlighted with bold-ed icons. Besides, users can return to the previous menu by clicking the left button or swiping from right to left. In addition, continuous manipulation and selecting from list are supported by scrolling circularly on the trackpad.

Additionally, to make the menu compatible with the ring mouse, the maximal number of items in each menu level was set to match the input mechanism of the ring mouse (four items in our prototype, as the ring mouse has four clickable buttons, see Figure 1c).


Based on the analysis of prior research, the promising features of OHMD and ring mouse motivated us to design ParaGlassMenu to support bi-directional interaction, specifically for IoT manipulation during social settings. In addition, to evaluate the performance and social acceptability of ParaGlassMenu, two studies were conducted answering the following research questions with the approval from our university's institutional review board (IRB).

  • RQ1: How does the ParaGlassMenu compare with other interfaces in terms of the quality of conversation and IoT manipulation?
  • RQ2: How does the ParaGlassMenu support IoT manipulation in real social settings?

5 Study 1: Comparison between the ParaGlassMenu and alternative interfaces

To answer RQ1, the ParaGlassMenu was compared against the smartphone touch-screen interface, voice user interface, and Linear OHMD menu interface. The comparisons were conducted in a simulated conversation setting to eliminate confounding factors in realistic settings and provide a consistent experience.

5.1 Participants

Twenty volunteers (12 females, 8 males, mean age = 22.10 years, SD = 1.65 years) from the university community participated in this study. None of these participants wore spectacles or had any vision deficiencies. Eleven participants reported that they used IoT devices (i.e., smart speakers (9), smart lamps (2)). Each participant was compensated ≈ USD 7.25/h for their time.

5.2 Comparative Interfaces

Three comparative interfaces were selected in this study for the following reasons. Firstly, current IoT manipulation interfaces have two main limitations in terms of 1) maintaining eye contact and 2) conducting opaque (discreet and invisible) IoT manipulation in conversation (see Table 1). Thus, to systematically evaluate ParaGlassMenu, it should be compared against interfaces with the two above limitations. In addition, prior studies (e.g.,[5]) compared the subtle interfaces with traditional interfaces in conversation settings to identify the relative advantages and disadvantages. Thus, two traditional interfaces, including phone (hard to maintain eye contact) and VUI (hard to support opaque manipulation), were selected as comparative baselines in this study. Secondly, because of previous results’ inconsistency on the preferred interface layout [36, 79], ParaGlassMenu was compared with a linear layout menu to further explore the most suitable layout for attention-maintaining bi-directional interactions in conversations. The details of these three comparative interfaces are as follows.

5.2.1 Smartphone touch-screen interface (Phone Interface). Design: Android Google Home app1 was used to manipulate IoT devices as it is commonly used in smart homes [27].

Interaction Methods: Users can press the power button to unlock the phone. Then, users can open and manipulate the mobile app (see Figure 3a) by tapping on the app's icons on the phone or scrolling the circular slider on the screen using touch interactions.

Figure 3
Figure 3: Two Comparative interfaces. (a) An instance of menu layout on the phone. The path shows how to control AC. (b) OHMD Linear Menu. The yellow arrows stand for using the top button and bottom buttons for menu navigation.

5.2.2 Voice user interface (Voice Interface). Design: Google Home platform, specifically Google Nest Hub 22, was used for interacting with IoT devices via voice [66]. Since our focus is voice-based interaction, its visual display was completely covered to support voice-only interactions.

Interaction Methods: Users can speak out “Hey Google” to activate the voice assistant and then speak out the device name, desired function, and the device's location to manipulate an IoT device. For example, users can say, “Hey Google, turn on the light in the living room” to manipulate (turn on) the selected device.

5.2.3 Linear Menu on OHMD with ring interactions (Linear Interface). Design: This interface consists of a vertical linear menu aligned to the left side of the conversation partner's face, following Vatavu et al. [79]. All the other aspects of this menu were identical to the ParaGlassMenu.

Interaction Methods: Similar to the ParaGlassMenu, menu items are shown on the OHMD upon activation. However, given its vertical layout, users only use the top and bottom buttons for navigating menus (Figure 3b). Additionally, clicking the right button enables users to select the chosen menu icon, while the left button enables them to return to the previous menu. Lastly, users can do continuous manipulations by scrolling vertically on the central trackpad.

5.3 Apparatus

Figure 4 shows the overall simulated conversation setting of the experiment. A virtual conversation partner (a muted talking head video following [36]) and two virtual rooms (a living room and a kitchen) were displayed on three 27” LCD monitors (refresh rate = 60 Hz, resolution = 1920 x 1080 px) at eye level. The former was modeled after an average female (head height = 24 cm [61], FoV = 9.15° vertical at 1.5 m) and was displayed on the central monitor 1.5 meters away from the participants following the social conversation distance defined by Hall et al.[29, 36]; while the latter were displayed on side monitors to provide an immersive feeling of a home at the same distance. A Python program controlled the virtual conversation partner and other stimuli on desktops. Note that the virtual conversation partner was used with a trade-off consideration between external validity and internal validity [53]. While using realistic conversation partners can enhance external validity, it can significantly reduce internal validity by introducing potential confounding factors, such as inconsistent replies in terms of content and duration, which can affect the users’ manipulation behaviors. Thus, we selected a virtual conversation partner to make a fair comparison in this study.

There were a total of eight IoT devices, four in the living room (two lights, an air-conditioner, and a smart speaker) and four in the kitchen (a light, a dishwasher, and two drink machines), following common smart home settings [23, 35, 37]. To manipulate the IoT devices, as shown in Figure 4, participants used either an OHMD (Nreal Light3, 1920x1080, 60 Hz, FoV ≈ 45° horizontal × 25° vertical) with a ring mouse (Sanwa 400-MAW151BK with 4 buttons and 1 touchpad), a smartphone (Google Pixel 4, 5.7”), or a smart speaker (Google Nest Hub 2) depending on the conditions. A mobile eye tracker (Pupil Core/Pupil Core Addon) was used either attached to OHMD or directly worn on the head. Four April Tags were attached to the central monitor for the eye tracker to register the location of the virtual conversation partner.

Figure 4
Figure 4: Overall setup of study 1. The participant sits 1.5 m away from the virtual conversation partner. Depending on the condition, the participant either wears an OHMD with a ring mouse or only uses the phone or the speaker (VUI) to manipulate virtual IoT devices.

For ParaGlassMenu and Linear Interface, participants wore the Nreal Light along with the ring mouse on their dominant hand. Menus were implemented using Unity4 and displayed at the same depth as the virtual partner using the mixed reality mode of Nreal. OpenCV plus Unity asset 5 was used to track the target face with the Nreal's camera, which positioned the menu around the face. The size of the menu icons, 5 cm in diameter, was designed based on a pilot study (N=5) where participants could recognize the menu while looking at the virtual face from a 1.5 m distance (Figure 1c and Figure 3b).

For the Phone Interface, participants used a Google Pixel 4 phone installed with the Google Home app and YouTube Music app6. YouTube Music app was used to select and stream songs to the smart speaker, as Google Home app doesn't allow playing songs directly in the app. The locked phone was placed on the table within hand reach. Moreover, for the Voice Interface, participants used integrated Google Voice Assistant in the Google Nest Hub 2. In addition, Google Home Playground7 was used to generate virtual IoT devices and rooms for Google Home App (Phone Interface) and Google Nest Hub 2 (Voice Interface).

5.4 IoT Manipulation Tasks Design

IoT manipulation tasks can be divided into two types: 1) information task in which users get information about a device and 2) command task in which users execute a command on a device [72]. Moreover, our analysis of the IoT tasks in smart home scenarios based on the Google Home Device traits [28] revealed six major sub-tasks related to IoT manipulations: 1) Activation: turning on the manipulation interface; 2) Navigation: going to the corresponding room/device; 3) Selection: selecting the room/device/item; 4) Checking: examining the state of the device; 5) Discrete manipulation: changing the discrete state of the device; and 6) Continuous manipulation: changing the continuous state of the device.

By aligning the two IoT manipulation task types with six sub-tasks, we found activation, navigation, and selection were common to both types. Besides, an information task involves checking; while a command task, depending on the capabilities of the device, includes discrete or continuous manipulation. Moreover, the task complexity, i.e., the number of steps or the duration required to complete a task, depends on the number of states supported by the device.

Thus, to evaluate the interfaces across different tasks with different complexities, four IoT manipulation tasks (i.e., IoT Tasks), including one information task and three command tasks, were selected to cover the full spectrum of sub-tasks. In that regard, Checking Info (i.e., check the device's current state) was selected as the information task; while Discrete Manipulation (i.e., change the active state of the device), Continuous Manipulation (i.e., change the continuous state of the device), and Selecting From List (i.e., change the discrete state of the device which has more than two states) were selected as command tasks. Appendix A.1 presents sample IoT tasks and Table 2 summarizes the interaction methods used for the selected IoT Task on the selected Interface.

Table 2: Interaction methods of IoT Task on different Interfaces
Interface Checking Info Discrete Manipulation Continuous Manipulation Selecting From List
ParaGlassMenu Clicking buttons Clicking buttons Clicking buttons + Scrolling Clicking buttons + Scrolling
Linear Clicking buttons Clicking buttons Clicking buttons + Scrolling Clicking buttons + Scrolling
Voice Speaking Speaking Speaking Speaking
Phone Tapping Tapping Tapping + Scrolling Tapping + Scrolling

5.5 Study Design

A repeated-measures within-subject design was used in which the independent variables were IoT Interface (ParaGlassMenu, Linear, Phone, Voice) and IoT Task (Checking Info, Discrete Manipulation, Continuous Manipulation, Selecting From List), resulting in 16 sessions per participant. Furthermore, the IoT Interface was counterbalanced using Latin Square across participants, and the IoT Tasks were presented in a fixed order with increasing complexity, i.e., Checking Info followed by Discrete Manipulation, followed by Continuous Manipulation followed by Selecting From List because comparing conversation and IoT manipulation quality across different task types was not in the scope of this research.

To avoid the potential biases due to the menu layouts, three trials for each IoT Task were designed, and each trial involved different devices with the same complexity. In summary, the final design involved 960 IoT trials in total, including: 20 participants × 4 Interfaces × 4 IoT Tasks × 3 trials per task.

5.6 Task and Procedure

After getting consent, participants were first given brief guidance and training sessions to familiarize themselves with each Interface; then completed the 16 sessions in the formal experiment.

For each session, the eye-tracker was first calibrated, then three trials were conducted. For each trial, the manipulation commands were first displayed in text form consisting of action, device name, and location (e.g., “Raise the Temperature of the AC Above 27 in the Living Room”, see Appendix A.1) on the central monitor for seven seconds to ensure participants can read the commands at least twice [15]. Next, the text “Start” was shown on the monitor for one second to tell participants they could start manipulating the device when the virtual conversation partner showed up; then, the virtual conversation partner was displayed on the central monitor and continuously speaking (moving mouth) until the participant successfully completed each trial (see the details of stimuli in Appendix A.2). We asked the participants to act as if they are listening to their conversation partner when manipulating.

To ensure consistent experience among all participants, the state of the IoT devices and the status of the Interface were reset to the default after each trial. After finishing all three trials for each session, participants filled out questionnaires, detailed in sec 5.7, about their experience with the corresponding Interface and IoT Task pair.

Moreover, participants were given a 10-minute break upon completing all four sessions for each Interface. After completing all sixteen sessions, they filled out a questionnaire with their overall rankings and attended 8-12 minutes of semi-structured post-interview. The entire experiment took approximately 120 minutes per participant.

5.7 Measures

Following our RQs, the quality of simulated conversation and IoT manipulation were evaluated using objective and subjective measures. Additionally, preference rankings for all interfaces and their reasons were collected.

5.7.1 Quality of (simulated) conversation. The Face Focus (i.e., the percentage of time the user's gaze within the bounding box between eyebrow and mouth, Figure 5) was used as the objective measure following Janaka et al. [36].

In addition, subjective measures were also collected, such as Politeness (‘I felt it is polite to use the system during the conversation’) and Naturalness (‘I acted naturally at all times while focusing on the face and manipulating IoT devices’) by adapting from previous studies on social setting [40, 57] using a 7-point (1 = Strongly Disagree, 7 = Strongly Agree) Likert scales. Lastly, the perceived task load for maintaining focus on conversation and manipulating IoT devices was collected using Raw NASA-TLX (RTLX [30]).

Figure 5
Figure 5: The Face Focus area is enclosed in green rectangle. The size of Face Focus area is 11cm x 11cm, following the female biocular breadth [61].

5.7.2 Quality of IoT manipulation. The Task Duration (i.e., the average time to complete the given IoT task in seconds from starting of IoT Task till completion feedback) and Task Accuracy (i.e., the ratio of the number of successful manipulation attempts relative to the total number of manipulation attempts) were utilized as objective measures. Furthermore, Relaxation (‘I felt relaxed while manipulating IoT devices’, using 7-point Likert scales [57]), and system usability score (SUS [14]) were collected as subjective measures.

5.7.3 Analysis. Factorial repeated measures ANOVAs or factorial repeated measures ANOVAs after Aligned Rank Transform (ART [82]), in cases of violation in ANOVA assumptions, were applied; and the normality and sphericity were tested using the Shapiro-Wilk test and the Mauchly test, respectively. Moreover, paired-sample t-test or Wilcoxon signed-rank test were used as post-hoc tests, and Bonferroni correction was applied to p-values for multiple comparisons. The interview recordings were transcribed and thematically analyzed following Braun and Clarke [13].

5.8 Results

During the study, a total of 320 data points were collected. Figure 6 and Figure 7 indicate the participants’ performance (see Appendix A.3 for details).

5.8.1 Quality of (simulated) conversation. Overall, there was a significant (p < 0.05) main effect of the type of Interfaces for all measures, and the ParaGlassMenu allowed the highest quality of conversation when compared to other interfaces.

Figure 6
Figure 6: Quality of simulated conversation. Error bars represent standard error. The middle lines of box plots represent median values. represents significant (p < 0.05) post-hoc tests and × inside box plot represents the mean value point. See Appendix A.3 for details.

Face Focus: A repeated-measures ANOVA after ART indicated significant main effects of Interface (F3, 285 = 85.155, p < 0.001), IoT Task (F3, 285 = 9.394, p < 0.001), and interaction effect (F9, 285 = 2.583, p = 0.007). Besides, there were simple effects (p < 0.05) for individual levels of Interface and IoT Task except for Phone Interface. Moreover, post-hoc analysis revealed Voice and ParaGlassMenu were significantly higher than Linear and Phone (pbonf < 0.05), with Linear significantly higher than Phone (pbonf < 0.05). There was no significant difference between ParaGlassMenu and Voice.

Overall, Voice enabled the highest Face Focus (M = 0.253, SD = 0.192) on the virtual conversation partner as it did not provide any visual feedback that deviated their visual focus from the conversation partner's face; however, six participants who disagreed with the above mentioned that they could focus better with ParaGlassMenu (M = 0.235, SD = 0.119) over Voice as they tended to look at the smart speaker before speaking; while the circular layout of ParaGlassMenu helped them concentrate on the face. In contrast, Phone had the lowest Face Focus (M = 0.044, SD = 0.043) as IoT manipulation using Phone required users to switch between the phone and the face.

Politeness: There was only a significant main effect of Interface (F3, 285 = 50.731, p < 0.001) and the post-hoc analysis revealed that ParaGlassMenu and Linear were significantly higher (pbonf < 0.001) than Phone and Voice, with no significant difference between other Interface pairs.

Overall, OHMD interfaces, particularly ParaGlassMenu showed the highest Politeness (M = 5.51, SD = 1.12) as it enabled participants to keep focus on the face. In contrast, participants felt it was “rude” and “impolite” to use the Phone (M = 3.73, SD = 1.84) to manipulate devices during a conversation as it required attention switching between the face and the phone and violated social norms. Similarly, participants felt using Voice (M = 3.84, SD = 1.75) was impolite and “awkward” as it could interrupt and pause the conversation; however, two participants mentioned that using Voice was acceptable to play songs when the conversation topics were related to songs as it could increase shared interactions.

Naturalness: There was only a significant main effect of Interface (F3, 285 = 12.800, p < 0.001) and the post-hoc analysis revealed ParaGlassMenu and Linear Interfaces were significantly higher (pbonf < 0.02) than Phone and Voice, with no significant difference between other Interface pairs.

Overall, ParaGlassMenu showed the highest Naturalness (M = 5.23, SD = 1.04) indicating that it allowed the manipulation of IoT devices with lesser interruption, according to post-interviews.

RTLX: There were only significant main effects of Interface (F3, 285 = 4.234, p = 0.006) and IoT Task (F3, 285 = 4.040, p = 0.008). Moreover, the post-hoc analysis revealed ParaGlassMenu was significantly lower (pbonf = 0.004) than Voice, with no significant difference between other Interface pairs.

Overall, the ParaGlassMenu had the lowest RTLX (M = 22.23, SD = 14.34) as it enabled easier IoT devices multi-tasking while focusing on the face. Additionally, ParaGlassMenu, Linear, and Phone provided visual cues, which reduced the burden of remembering the commands or making mistakes compared to Voice. In contrast, Voice caused the highest RTLX (M = 27.67, SD = 19.99) due to command recognition errors which made participants repeat voice commands. Moreover, as expected, it made users “wait” for confirmation feedback which took longer time-demand than other Interfaces.

5.8.2 Quality of IoT manipulation. Overall, there was a significant (p < 0.05) main effect of Interface for all measures, and the ParaGlassMenu increased the quality of IoT manipulation over others.

Figure 7
Figure 7: Quality of IoT manipulation. Error bars represent standard error. The middle lines in the box plot represent median values. represents significant (p < 0.05) post-hoc tests and × inside box plot represents the mean value point. See Appendix A.3 for details.

Task Duration: There were significant main effects of Interface (F3, 285 = 321.711, p < 0.001), IoT Task (F3, 285 = 58.370, p < 0.001), and interaction effect (F9, 285 = 15.496, p < 0.001). Besides, there were simple effects (p < 0.05) for all individual levels of Interface and IoT Task. The post-hoc analysis revealed significant differences (pbonf < 0.001) between all Interface pairs with the ParaGlassMenu having the lowest duration and Voice having the highest.

Overall, ParaGlassMenu had the lowest Task Duration (M = 5.75, SD = 2.28) as it enabled to locate and navigate individual devices easily while maintaining focus on the face, provided “more intuitive” manipulation compared to Linear, and reduced attention switching between the face and the menu compared to Phone. On the contrary, as expected, Voice had the highest Task Duration (M = 14.18, SD = 5.60) due to the longer time to provide voice commands and get feedback, and multiple attempts due to voice recognition errors.

Task Accuracy: There were significant main effects of Interface (F3, 285 = 64.194, p < 0.001), IoT Task (F3, 285 = 100.873, p < 0.001), and interaction effect (F9, 285 = 20.279, p < 0.001). Besides, there were simple effects (p < 0.05) for Voice and IoT Tasks except for Discrete Manipulation. The post-hoc analysis revealed ParaGlassMenu, Linear, and Phone were significantly higher (pbonf < 0.001) than Voice, with no significant difference between other Interface pairs.

Overall, Voice had the lowest accuracy (M = 0.844, SD = 0.183) due to the speech recognition inaccuracy, which led to repeated commands. On the contrary, ParaGlassMenu has the highest accuracy (M = 0.997, SD = 0.028) due to its intuitive spatial mapping, and Phone has the second highest accuracy (M = 0.994, SD = 0.039) due to its familiar UI designs with touch interaction.

Relaxation: There was only a significant main effect of Interface (F3, 285 = 12.523, p < 0.001) and the post-hoc analysis revealed Voice was significantly lower (pbonf < 0.05) than other Interfaces, and Linear was significantly lower (pbonf < 0.05) than ParaGlassMenu. There was no significant difference between other Interface pairs.

As expected, Phone had the highest Relaxation (M = 5.69, SD = 0.91) due to device familiarity. ParaGlassMenu has the second highest Relaxation (M = 5.68, SD = 1.13) due to its quick and intuitive manipulation. While Voice was felt the least relaxed (M = 4.80, SD = 1.69) as incorrect recognition of voice commands caused repeated attempts and delays in feedback.

SUS: A one-way repeated-measures ANOVA with Greenhouse-Geisser correction (ϵ = 0.71) revealed a significant effect of Interface (F2.155, 40.952 = 5.288, p = 0.008, η2 = 0.218; Note: SUS was calculated only for each Interface). The post-hoc analysis revealed that Voice was significantly lower (pbonf < 0.05) than ParaGlassMenu, Linear, and Phone, with no significant difference between the pairs of the three Interfaces.

Overall, ParaGlassMenu was perceived as the most usable system (M = 83.00, SD = 9.82) to manipulate IoT devices in a conversation setting as it was “intuitive”, “easy to use”, “polite”, “faster than others”, and “help[ed] to concentrate on people's face”. In contrast, Voice had the lowest SUS score (M = 70.88, SD = 18.84), which was below the threshold (i.e., 80 [11]) for good usability.

5.8.3 Preference rankings. Figure 8 indicates the overall preference ranking of Interfaces.

Figure 8
Figure 8: Preference ranking for different Interfaces (N=20).

The majority of participants (12) ranked ParaGlassMenu as their most preferred Interface, while Voice is the least preferred one (11). They reported that ParaGlassMenu was intuitive, easy to use, polite, and less distracting to the conversation than the other Interfaces, while Voice could interrupt conversations as voice commands could pause the conversation and speech recognition errors cause repeated attempts.

The participants (5) who selected the Phone as their first preference mentioned that familiarity helped them to control the IoT devices easily and conveniently, and it was acceptable as “most people have gotten used to people occasionally checking their phones”. At the same time, two participants who chose Voice as their first preference mentioned that it took them less effort, did not affect their focus on the partner, felt more natural, and was easier to use when compared to ring mouse or phone. Lastly, the remaining participant who chose Linear as the first preference mentioned that the 1D nature of Linear was simpler and easier to locate than the 2D nature of ParaGlassMenu.

5.9 Discussion

Overall, the ParaGlassMenu achieved the highest conversation quality in terms of more focus on the conversation partner ($M = 23.5\%,\: SD = 11.9\%$), the highest politeness (M = 5.51, SD = 1.12 / 7) and naturalness (M = 5.23, SD = 1.04 / 7), and the lowest cognitive load (M = 22.23, SD = 14.34 / 100). ParaGlassMenu also enables the most effective IoT manipulation measured with the lowest IoT manipulation time (M = 5.75, SD = 2.28 s), the highest accuracy ($M = 99.7\%,\: SD = 2.8\%$) and best usability score (M = 83.00, SD = 9.82 / 100) in a relaxed manner (M = 5.68, SD = 1.13 / 7). Thus, manipulation of IoT devices with ParaGlassMenu demonstrated the lowest interference to the conversation. Furthermore, it was also the most preferred Interface. Linear Interface is recommended as the second choice due to its familiar linear layout, but interacting with it requires much higher attention, which causes a noticeably lower focus on the conversation partner.

On the other hand, as expected, Phone and Voice Interfaces have limitations in a conversation setting, given the Phone failed to support high conversation quality and Voice failed to support both high conversation quality and high usability in social interactions. But this does not mean Phone and Voice Interfaces should be excluded. Phone Interfaces are the most accessible interface today, and it is the most familiar, making them the easiest and default choice for most users. Voice Interface has the ability to maintain visual attention on a target and be accessed ubiquitously, which can be particularly useful in other non-social settings, such as single-user scenarios and driving scenarios.

6 Study 2: Validate the ParaGlassMenu in a realistic setting

The results of study 1 indicated the suitability of ParaGlassMenu during conversation settings in terms of quality of conversation and IoT manipulation in a controlled simulated laboratory setting. To further verify the external validity of this finding and answer RQ2, we conducted a second study in a modeled realistic setting to evaluate the usage of ParaGlassMenu.

6.1 Participants

Twenty four participants (who did not participate in study 1 or pilot studies) in twelve pairs (6 female pairs, 5 male pairs, 1 mixed gender pair, mean age = 23.00 years, SD = 2.22, P1 − P12Host/Guest) from the university community participated in this study. Following previous studies [57, 60], we chose the pairs of individuals who were familiar with each other to generate natural conversations. For each pair of participants, one was randomly chosen as the host while the other acted as the guest. Moreover, all participants self-reported to be fluent in English. They had normal or corrected to normal visual acuity without color deficiencies. None of them had prior experience using OHMDs, while ten of the participants had smart devices in their homes and self-reported to spend 1-10 minutes per day controlling IoT devices via phones or voice assistants. Each participant (host/guest) was compensated ≈ USD 7.25/h for their time.

6.2 Apparatus

Figure 1a shows the overall setting of the experiment. Similar to study 1, a local apartment was modeled in the lab with a living room and a kitchen. A sofa, a chair, and a table were placed in the living room, and a dining table was placed in the kitchen.

A total of four IoT devices were selected following past field studies on smart homes [23, 35, 37]. Three of them were in the living room: a fan (Xiaomi Mi Smart Standing Fan 2), a lamp (Philips Hues and Bridge), and a music player (Macbook Pro's speaker); and one was in the kitchen: a water heater (with Xiaomi Mi Smart Plug 2). To manipulate IoT devices, the host wore the OHMD (Nreal Light, with circular menu, enabling face-tracking) and the ring mouse (Sanwa 400-MAW151BK), which were identical to study 1.

6.3 Study Design

A repeated-measures within-subject design was used. Each pair of participants had two conversations, i.e., with or without IoT manipulation (IoT, No_IoT), and the order was counterbalanced. Among them, the No_IoT condition, as a baseline, measured the conversation's quality without the usage of ParaGlassMenu and minimized potential confounding factors due to current limitations with OHMDs, such as weight and appearance [38, 43]. In summary, the design was as follows: 12 participant pairs x 2 types of conversation = 24 conversation sessions in total.

6.4 Tasks

Participants had two tasks during the conversation. The primary task required hosts and guests to converse casually on any topic for around 15-20 minutes [55] in both IoT and No_IoT conditions.

No specific secondary tasks were required for participants in the No_IoT condition, but they could adjust the room conditions before starting the conversation; while in the IoT condition, the secondary task for hosts was to freely manipulate the IoT devices based on their preferences or suggestions from guests (who were aware that they could express their needs for IoT control to the hosts) if they had any, which included turning on/off the lights/fans, changing the speed of fan/brightness of light, playing/pausing music in the living room; remotely turn on the kettle, and preparing tea/milk/water in the kitchen.

6.5 Procedure

After getting consent, the hosts were given a briefing and training session to familiarize the ParaGlassMenu and the IoT devices in the modeled home, while the guests were briefed about the task in a separate room. When both participants were ready, the guest was guided into the modeled home and started the conversation at a distance of 1.5 meters from the host. The experimenters monitored (and video recorded) the conversation and IoT manipulations via teleconferencing in a separate room. To provide natural opportunities to control IoT devices, at the beginning of the study, the room temperature was set to 28°C, and lighting was set to 50 lux representing dark indoors [45].

After finishing each conversation session, the host and the guest filled out separate questionnaires (sec 6.6) about their experiences. They were given a five-minute break between the conversation sessions. After completing the two conversation sessions, they separately attended 10-15 minutes semi-structured interviews. The entire experiment took approximately 75 minutes per participant pair.

6.6 Measures

Similar to study 1, the quality of conversation and IoT manipulation were measured using objective and subjective measures. In addition, both the host's and guest's perceptions of their conversation, IoT manipulation behavior, and associated advantages/disadvantages of using the ParaGlassMenu Interface were captured in the interviews.

6.6.1 Quality of conversation. For both IoT and No_IoT conditions, subjective measures from hosts and guests on attention and concentration, eye contact, naturalness, and perceived impact of OHMD were collected following measures developed by McAtamney et al. [57] (see Table 3).

6.6.2 Quality of IoT manipulation. For the IoT condition, Task Duration and the usage of IoT manipulation (e.g., the number of times they used each device) were collected to assess how IoT manipulation performance was in the modeled setting.

In addition, subjective measures, such as Relaxation and SUS were collected from the hosts, similar to study 1. Besides, IoT Interruption (“Device manipulation by (me/my partner) did interrupt the conversation”, 1 = Strongly Disagree, 7 = Strongly Agree) and Politeness were collected from both the hosts and guests, following [40]. Lastly, the rating of Hospitality (“My partner treated me well during the conversation, satisfying my needs by controlling some appliances”) was collected from guests using a 7-point Likert Scale.

Table 3: Aspects and measures adopted from McAtamney et al [57] on conversation behavior of the host from hosts’ and guests’ points of view. Each measure is rated using a 7-point Likert scale.
Aspect on conversation Measures
Attention and concentration AC1 (listening to guest): [host] ‘When the other person was speaking, I was always listening to them’ / [guest] ‘When I was speaking, I think the other person was always listening to me’
AC2 (concentration on conversation): [host] ‘I was always concentrating on the conversation’ / [guest] ‘I think the other person was always concentrating on the conversation’
AC3 (attention on guest): [host]‘When I was speaking, my attention was towards the other person’ / [guest] ‘When the other person was speaking their attention was towards me’
Eye contact EC1 (eye contact with guest): [guest] ‘When I was speaking the other person maintained eye contact’
Naturalness NB1 (host acting naturally): [host] ‘I acted naturally at all times during the conversation’ / [guest] ‘The other person acted naturally at all times during the conversation’
NB2 (host feeling relaxed): [host] ‘I felt relaxed during the conversation’/ [guest] ‘ The other person appeared relaxed during the conversation’
Impact of OHMD IO1 (ignoring glasses): [host] It was easy to ignore the fact that I was wearing smart glasses / [guest]‘It was easy to ignore the fact that the other person was wearing smart glasses’

6.6.3 Analysis. Wilcoxon signed-rank test was used when the baseline data was present, or else descriptive statistics statistically were used to analyze the results. Lastly, the interview recordings were thematically analyzed in a manner similar to study 1 (sec 5.7.3).

6.7 Results

Overall, the results indicated that hosts could manipulate IoT devices in a relaxed and polite manner using the ParaGlassMenu Interface to cater to both the hosts’ and guests’ needs with low interference to conversations. Figure 10a and Figure 10b show both the objective and subjective mean performance measures (see Appendix B.1 for details).

6.7.1 Overall experience. On average, the IoT sessions lasted 19.1 minutes (SD = 1.4) and each host carried out 7.5 IoT tasks (SD = 2.65). While the No_IoT sessions lasted 18.8 minutes (SD = 1.5). Note that the number of IoT tasks performed by each participant is higher than normal (≈ 3 voice commands per hour with “Alexa” [73, Fig 2]), which helps us to validate the ParaGlassMenu in more extreme usage.

In addition, as expected and shown in Figure 9, most IoT tasks were carried out by the hosts at the start of the conversation (M = 4.58, SD = 2.39 within the first 8 min; M = 2.92, SD = 1.73 for the rest of time) to set up the environment, such as turn on the light and fan. Furthermore, hosts carried out IoT tasks to entertain guests (e.g., play music) or to satisfy their guests’ on-demand needs (e.g., adjust the light when the guest was scanning a magazine).

Figure 9
Figure 9: IoT task distribution during the conversation. The X-axis represents the start time with respect to total conversation duration, while Y-axis represents the total IoT task count for all 12 participant pairs.

6.7.2 Quality of conversation.

Figure 10
Figure 10: Quality of Conversation and IoT manipulation. (a) Conversation Quality ratings by hosts and guests during conversations for both IoT and No_IoT conditions (N = 12 x 2). The maximum score for each measure is 7. and † represent significant (p < 0.05) post-hoc tests and × inside box plot represents the mean value point. The definition of the abbreviations used in the x-axis (e.g., AC1) can be found in Table 3. See Appendix B.1 (Table 7 and Table 8) for details. (b) Subjective ratings of Quality of IoT Manipulation by hosts and guests during conversations (N = 12 x 2). × inside box plot represents the mean value point. See Appendix B.1 (Table 9) for details.

Impact of usingParaGlassMenu: Overall, as shown in Figure 10a, both the hosts and the guests rated the conversation related scores as high (i.e., 25th percentile value above 5 out of 7) in terms of AC1 (listening to guest), AC2 (concentration on conversation), AC3 (attention on guest), EC1 (eye contact with guest), NB1 (host acting naturally), and NB2 (host feeling relaxed) for both IoT and No_IoT conditions. These results support that IoT manipulation with ParaGlassMenu had a low impact on the overall quality of conversation because it enabled the hosts to maintain attention and concentration on the guests and behave naturally during the conversation. Note that the relatively low scores on IO1 (ignoring glasses) largely came from the limitation of Nreal glasses, such as their non-negligible weight and semi-transparent lens (sec 6.8.3). We expect such drawbacks can be largely eliminated with lighter and more transparent glasses in the future [1].

Influence of multitasking: As expected, due to the need to multitask, IoT condition scored lower in certain measures: AC2 (concentration on conversation) for both hosts and guests, AC3 (attention on guest), NB1 (host acting naturally) for guests, as indicated by the significant differences (p < 0.05) labelled in Figure 10a . However, all these scores in IoT condition were still high (i.e., 25th percentile value above 5 out of 7). In addition, the feedback in the interview suggested that while participants could notice the above difference between the two conditions, the magnitude of the difference was acceptable as “we [both hosts and guests] maintained good conversations in both sessions. (P2Guest)”

Comparison with other interfaces: A few participants (2 hosts and 3 guests) felt that the ParaGlassMenu helped them to be much more engaged in conversation as compared with past experiences of using either the Phone or Voice Interface to control IoT devices, because “the ring mouse is subtle enough that you can use it without interrupting the flow of the conversation. (P12Host)”, “If the host uses phone and voice to control the devices, the conversation could be paused during his operations. (P6Guest)”

6.7.3 Quality of IoT manipulation. Task duration: Table 4 shows the descriptive statistics of the IoT tasks. Compared with study 1, the average IoT task duration in this study was generally higher because most tasks were not urgent, and the hosts attempted to make a “cozy environment” for guests in a relaxed manner.

Table 4: The average IoT task count and task duration (in seconds; in order ‘mean (sd) [min, max]’) per participant pair.
IoT Task Manipulation Type Average Task Count Task duration
Adjust Light Continuous Manipulation 1.92 9.04 (4.48) [2, 20]
Adjust Fan Continuous Manipulation 1.83 9.77 (5.58) [3, 26]
Play/Pause Song Discrete Manipulation 0.5 7.00 (6.13) [1, 18]
Select Song Selecting From List 1.83 16.59 (9.69) [5, 43]
Turn on Kettle Discrete Manipulation 0.92 6.45 (3.11) [3, 13]
Check Kettle Checking Info 0.5 3.17 (0.41) [3, 4]

SUS, Politeness, Interruption, and Relaxation: Hosts found that the ParaGlassMenu had high usability (SUS score: M = 84.583, SD = 8.45), and both hosts and guests rated high Politeness for ParaGlassMenu as it enabled “convenient and fast manipulation (P3Host)” with low IoT Interruption to the conversation (see Figure 10b); yet, they still faced several issues, which are described below (sec 6.8.3).

Besides, all hosts felt relaxed when they manipulated the devices during their conversation and they elaborated in the interviews that controlling devices via the ParaGlassMenu can “reduce [users’] manual labor, increase their conversation time, and make the conversation smoother.” (P1Host)”

Hospitality: All guests rated high Hospitality from hosts, as hosts could attend to their requests fluently without interrupting the conversation, which made them feel more “welcomed and comfortable”.

6.8 Discussion

The results verify the usability of ParaGlassMenu in realistic conversations. In the aspect of subtle interactions, the non-intrusiveness of ParaGlassMenu helped hosts focus easily on their partner's face and reduced the frequency in which “missing important information [non-verbal cues] from the partner's conversation (P6Host)”. And the host's subtle interactions not only provided a comfortable conversation environment but also avoided unnecessary interruption and made the conversation flow smooth.

In addition, users’ manipulation behaviors in realistic conversations and the current prototype's limitations are discussed.

6.8.1 Menu deactivation. Although manual menu deactivation was supported to minimize the unnecessary visual intrusiveness during conversations, the majority of the hosts (8) did not deactivate it. There are two reasons. Firstly, when participants focused on the conversation partner, the menu items circularly displayed in the peripheral vision remained non-intrusive to their focus on the partner. Secondly, participants tended to keep the menu on after starting pending tasks (e.g., hosts turn on the kettle, but it's not yet boiled) as it “saved time” on checking the task's status without being noticed by others. This suggests auto menu deactivation for non-pending tasks. In addition, upon menu activation, quickly resuming pending tasks helps to reduce manipulation time.

6.8.2 Manipulation visibility. Besides immediately satisfying the IoT manipulation requests from the guests, two patterns of visibility of IoT manipulation were observed for all the hosts. First, hosts attempted to manipulate devices discreetly when the guest was speaking or during a pause in the conversation. Second, they verbally highlighted their manipulation task to their partner before the manipulation (e.g., “let me turn on the light”).

The different manipulation visibility indicated that users make decisions on awareness and social etiquette management in conversations; specifically, the visibility of hosts’ manipulations depended on whether the guest could notice the effects of manipulation (e.g., light brightness’ change) and whether the manipulation is relevant to the conversation. Generally, hiding digital interactions irrelevant to the conversation (e.g., turning on the kettle in the kitchen) could help hosts avoid distractions to others. However, if the guest could notice the effects of manipulation without being informed, they would realize that the host was distracting from the conversation, which violated the social norms [24]. In this case, if the manipulation is relevant to the conversation topic or involved parties, hosts would inform the guests in advance to avoid unexpected distractions and impoliteness to the conversations. The design implications for the manipulation visibility are discussed in sec 7.2.2.

6.8.3 Limitations with current prototype. Several hardware issues were reported by the participants. Similar to study 1, a few hosts (2) found that using ‘scrolling’ to select songs or change the brightness/speed of light/fan was not precise enough, which led them to select the wrong options. Besides, guests could sometimes notice the button click sound, but all of them mentioned it was acceptable when they knew manipulations were done to cater to their needs. In addition, all hosts noted that the OHMD was a bit heavy and slippery, thus not convenient to wear for a long time. All guests mentioned that the black lenses made OHMDs harder to ignore.

Moreover, unfamiliarity issues also existed at the start of the study. For example, a few hosts (2) mentioned that they forgot how to “clear” the menu (i.e., deactivate ParaGlassMenu Interface) and needed several attempts at the beginning of the conversation. To minimize such issues and support novice users, help ‘hints’ indicating the ring mouse mapping with the next possible tasks in the current menu can be used.


7.1 Using ParaGlassMenu in conversation

7.1.1 Quality of conversation and IoT manipulation. ParaGlassMenu has been found to facilitate higher-quality conversation when interacting with a secondary digital task. In comparison with other interfaces, it has been shown to have comparable Face Focus with Voice and significantly higher Face Focus than that of Linear Interface and Phone. Furthermore, ParaGlassMenu has demonstrated the highest level of Politeness and Naturalness, as well as the lowest RTLX score.

ParaGlassMenu has also been found to improve the quality of IoT manipulation. In comparison with other interfaces, it has been shown to have the lowest Task Duration, highest Task Accuracy, highest SUS score, and second-highest Relaxation score. These findings suggest that ParaGlassMenu is a promising interface for facilitating IoT manipulation during social conversations.

Our results were further validated in more realistic conversation settings, as participants showed that ParaGlassMenu can support digital interaction with low interference to conversations.

7.1.2 Ability to multitask during social interactions. The majority of participants (10/12 in study 2) reported that they were able to effectively manage both conversation and IoT manipulation using ParaGlassMenu due to the non-intrusive nature of its visual feedback and discreet interactions. This is in contrast to previous research on subtle interaction, such as jaw-teeth [8] and silent-speech interaction [46], which is challenging to use during social conversations. However, there were instances where participants struggled to maintain attention when manipulation of IoT tasks became more complex (e.g., selecting songs). In these cases, the increased cognitive demands of visual searching and decision-making interfered with their ability to speak and maintain eye contact and resulted in a slight decrease (7% on average) in conversation quality.

Despite these challenges, we observed that participants employed various strategies to mitigate the negative impacts of complex IoT manipulation on conversation quality. One approach (used by 6 out of 12 participants) involved manipulating the IoT device while listening, or during pauses in the conversation, in order to reduce competition for mental resources and minimize interference with speaking, aligned with the theories of capacity sharing and bottleneck model [62]. Another strategy (used by 3 participants) was to delay and slow down manipulation until a less important moment in the conversation, rather than immediately reacting to interaction needs. Additionally, one participant (P11Host) reported that the negative impact of digital interaction on conversation quality can be reduced with practice. Previous research has shown that with sufficient training, a task can be performed "automatically" without consuming significant mental resources [17, 39].

7.1.3 Visibility of Manipulation. Given users have different interaction needs in different scenarios (sec 2.1), the ability to flexibly select the visibility of IoT manipulation can help users to follow social norms. The selection is determined by the visibility of effects (sec 6.8.2, e.g., whether producing transparent or discreet manipulation effects) and type of manipulation (i.e., whether it is relevant or irrelevant to the conversation). For example, in order to satisfy the needs of conversation partners and avoid unexpected environmental changes, users conducted over half of the IoT manipulations (51 out of 90 manipulations) transparently by verbally mentioning their actions in advance. Transparent manipulations were also necessary for tasks that involved shared control, such as selecting background music according to guests’ preferences (14 out of 51 transparent manipulations).

In contrast, when the digital manipulation tasks were irrelevant to the conversation topics (39 out of 90 manipulations), users chose to perform them discreetly (i.e., opaque) to minimize interference with conversations. Furthermore, participants reported that privacy concerns [54] can also influence the visibility of digital interaction. For example, P10Guest discussed a scenario in which he was engaged in a conversation with a guest while his baby was sleeping in another room. He needed to check on the baby's status periodically using a monitor app on their phone, and suggested that ParaGlassMenu could be particularly useful in this scenario. Overall, participants found the ability to discreetly access urgent private information using ParaGlassMenu to be useful and appealing.

Considering users’ needs for both transparent and opaque manipulations, designing an interface that supports both types of visibility is necessary (see details in sec 7.2.2). This allows users to select the appropriate level of visibility for the current interaction context and social norms. By providing this level of flexibility, the interface can better support the varied interaction needs of users in different scenarios.

7.1.4 Supporting other application scenarios. In addition to facilitating IoT control, ParaGlassMenu can be used in a variety of application scenarios. For example, in the scenario mentioned in the introduction, John could use ParaGlassMenu, as shown in Figure 11a, to discreetly select a default message response to reply to Nicole (e.g., “Can I call you later?”) without significantly interrupting his face-to-face conversation.

While ParaGlassMenu is designed for social interaction, it can also be used for other scenarios where users have to focus on a visual target while performing digital operations simultaneously. An example scenario is shown in Figure 11b, where one can utilize the ParaGlassMenu to record the important points in a class while remaining concentrated on the lecturer. With a few clicks, a student can start/stop recording the video and highlight important moments, which provides convenience for reviewing the lecture. Besides, the circular progress bar with text presents the recording time non-intrusively for students. Another application scenario can be drone interaction. With ParaGlassMenu, the user can perform commands such as controlling the flight and taking photos while maintaining attention to the drone's position and motion in the sky.

Figure 11
Figure 11: Other application scenarios for ParaGlassMenu. (a) Users can receive a message in a physical conversation and subtly select a default response to reply to the remote social inquiry with ParaGlassMenu. (b) A student can record videos using ParaGlassMenu while maintaining attention on the teacher.

7.2 Limitations and potential enhancements for the ParaGlassMenu

Based on the findings from the two studies, several issues with the current prototype of ParaGlassMenu were identified, including hardware and implementation-related concerns. As such, the following recommendations are suggested to enhance the usability of the ParaGlassMenu.

7.2.1 Enhancing thumb-index interaction. We used an off-the-shelf ring mouse for the ParaGlassMenu prototype, but it did not always provide precise scrolling and only supported a maximum of four clickable items. To improve the interface, a new ring mouse can be designed with a click wheel similar to iPod Classic8 or earPod [83] or using DeformWear's technology [80] for multiple item selection. This would enable smooth scrolling and support a larger number of clickable items, improving the usability and functionality of the ParaGlassMenu prototype.

7.2.2 Supporting two visibility of manipulation. ParaGlassMenu is designed to support discreet rather than transparent interactions. Currently, transparent interactions are achieved by the host verbally informing the guest of their intentions beforehand. In the future, the design of ParaGlassMenu could consider introducing a transparent mode that provides visual or audio cues to the conversation partner during the interaction. For tasks that involve shared decision-making (e.g., selecting a suitable song), using natural language processing to analyze the conversation between the host and guest may be a more natural way to achieve this task. This could involve identifying relevant keywords or phrases in the conversation and using them to filter the options for song selection, reducing the search space and enabling more efficient decision-making. These enhancements could improve the support for transparent tasks and further enhance the user experience in social settings.

7.3 Trade-off of subtle interactions in social settings

By using discreet manipulation with attention-maintaining visualization, users can attend to their digital interaction needs with minimum distraction to their primary social interaction. This can be particularly useful for handling the social needs of multiple relationships, such as maintaining a physical conversation with one person while also responding to remote social inquiries. While we highlighted the benefits of using subtle interactions in the social context, it nevertheless has trade-offs. One potential downside of subtle interactions with digital information is that they may be misused, leading to increased distractions during social interactions. For example, if users find it easier to interact with their digital devices in a subtle manner, they may be tempted to do so more frequently, even in situations where it is not appropriate. Another potential downside of subtle interactions with digital information is that they can cause misperceptions between conversation partners. In study 2, one guest reported a higher estimated number of manipulations by the host than was actually the case. This suggests that when people are aware of the possibility of subtle interactions, they may misinterpret natural behaviors, such as head movements or shifts in eye focus, as manipulations of digital information.

To prevent potential misuse, it's essential to make it easy to limit the usage of technology according to social contexts and provide an option to switch to "focused" mode, where attention can be solely devoted to the conversation partner. Additional visualizations, such as using low opacity for menu items that are not relevant to the conversation, can also help to restrict misuse.

To avoid misperception, users may need to pay more attention to their behaviors or be more transparent. For instance, when performing digital tasks that are related to the conversation, users can use the transparent mode to make it clear to their conversation partner that they're not disengaged. Alternatively, users can explain their motivation for discreet manipulation at the start of the interaction, so that their partner understands why they may need to use technology during the conversation.

In summary, while the use of subtle interaction techniques such as ParaGlassMenu in social contexts can offer many benefits, it is important to consider the potential drawbacks and take steps to mitigate them. Responsible design practices and attention to behaviors and transparency can help prevent misuses and misperceptions. Overall, the use of subtle interactions can enhance social interactions when used thoughtfully and with awareness of potential impacts.


Despite the results obtained from these studies supporting ParaGlassMenu, there are some limitations that need to be considered. First, in study 1, all participants didn't wear spectacles due to eye tracking. However, the overall preference could be affected by the spectacle-wearing experience. In addition, participants’ past experience and lack of familiarity with selected interfaces might also affect the task completion at the beginning. Furthermore, while a virtual conversation partner and immersive virtual home environment were utilized after considering trade-offs, the external validity of Naturalness and Politeness in such conversation settings is limited compared with real conversations, and thus, further investigation is required.

Second, in study 2, although participants had sufficient time for training, users may still not be fully familiar with the interface and modeled room at the beginning of the experiment. In addition, only a limited number of IoT devices were provided in the modeled room due to space limitation.

Third, the OHMD used in our studies is still not ideal for long-term everyday usage. Future OHMDs with lighter-weight, transparent lenses, better computational capabilities, and longer battery life can significantly improve their comfort and social acceptability.

Finally, the study participants were selected from the local university community, as this tech-savvy group has more experience with smart device usage and is more likely to be early adopters of OHMDs and our technique. However, it's important to note that social acceptance and device usability may vary across different cultural backgrounds and age groups. To generalize the results and better understand the long-term effects, longitudinal studies with different user groups using various IoT devices and OHMD prototypes need to be conducted. This will help to identify any differences in user experiences and preferences, and inform the development of more inclusive and user-friendly technology.


We utilized non-intrusive circular OHMD menus with discreet thumb-index interactions to support digital interactions in social settings and studied its usage as a socially friendly IoT manipulation interface. By comparing the proposed ParaGlassMenu with Phone, Voice, and Linear interfaces and testing the usage of the ParaGlassMenu in both simulated and more realistic conversation settings, it was verified that ParaGlassMenu could largely support a variety of interactions during face-to-face social conversations, making it an effective attention-maintaining subtle interface. Future work can explore other application scenarios of ParaGlassMenu designs and incorporate additional feedback in audio to further enhance humans’ abilities to handle their interaction needs in social settings.


This research is supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG2-RP-2020-016). It is also supported in part by the Ministry of Education, Singapore, under its MOE Academic Research Fund Tier 2 Programme (MOE-T2EP20221-0010), and by a research grant #22-5913-A0001 from the Ministry of Education of Singapore. In addition, this research is part of the programme DesCartes and is supported by the National Research Foundation, Prime Minister's Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. We thank all members of the NUS-HCI lab who helped to complete this project, Zhuoya Yang for helping draw figures, and all reviewers for their valuable feedback.

A Study 1

A.1 Samples of IoT Manipulation Task

Table 5 lists task samples we used in study 1.

Table 5: Samples of IoT Tasks (CI = Checking Info, DM = Discrete Manipulation, CM = Continuous Manipulation, SL = Selecting From List).
IoT Task Sample instructions
CI Check whether “Light 2” is On in the Living Room
Check whether “Dishwasher” is Off in the Kitchen
DM Turn On the “Coffee Machine” in the Kitchen
Turn Off the “Top Light” in the Kitchen
CM Raise the Temperature of “AC” Above 27 in the Living Room
Decrease the Brightness of “Top Light” Below 20 in the Kitchen
SL Play Taylor Swift's “Willow” in the Living Room
Play Justin Bieber's “Intentions” in the Living Room

A.2 Stimuli in study 1

Figure 12 shows the stimuli of study 1’s experiment.

Figure 12
Figure 12: The instruction procedure of one manipulation trial

A.3 Measures in study 1

Table 6 indicates participants’ mean performance (‘mean (sd)’) related to the quality of conversation and IoT manipulation.

Table 6: Measures in simulated conversation setting (N = 20). The first column represent the Interface-IoT Task combination using the first letters of each (C = ParaGlassMenu, L = Linear, P = Phone, V = Voice; CI = Checking Info, DM = Discrete Manipulation, CM = Continuous Manipulation, SL = Selecting From List, Avg = Average across all IoT Tasks). Highlighted text in each column indicates the best average value across Interfaces. Note: SUS is measured for Interface.
Quality of conversation Quality of IoT manipulation
Face Focus Politeness Naturalness RTLX Task Duration Task Accuracy ation SUS
C-CI 0.275 (0.104) 5.55 (1.317) 5.15 (0.988) 21.733 (11.832) 3.834 (1.062) 0.988 (0.056) 5.65 (1.089)
C-DM 0.281 (0.121) 5.55 (1.317) 5.35 (1.089) 20.733 (14.180) 5.075 (1.255) 1.000 (0.000) 5.80 (1.056)
C-CM 0.252 (0.123) 5.45 (1.468) 5.20 (1.152) 24.192 (16.563) 5.686 (1.980) 1.000 (0.000) 5.80 (1.105)
C-SL 0.133 (0.050) 5.50 (1.357) 5.20 (1.005) 22.250 (15.280) 8.398 (1.823) 1.000 (0.000) 5.45 (1.317)
C Avg 0.235 (0.119) 5.51 (1.12) 5.23 (1.04) 22.23 (14.34) 5.75 (2.28) 0.997 (0.028) 5.68 (1.13) 83.00 (9.82)
L-CI 0.129 (0.142) 5.50 (1.192) 5.35 (0.933) 23.592 (13.067) 4.772 (1.881) 0.988 (0.056) 5.30 (1.129)
L-DM 0.183 (0.203) 5.40 (1.046) 4.95 (1.317) 23.400 (13.469) 5.685 (1.552) 0.988 (0.056) 5.50 (1.100)
L-CM 0.157 (0.161) 5.40 (1.142) 5.15 (1.089) 22.900 (14.040) 6.016 (1.230) 1.000 (0.000) 5.60 (0.883)
L-SL 0.088 (0.102) 5.20 (1.152) 5.10 (0.912) 26.275 (16.767) 10.735 (2.203) 0.988 (0.056) 5.25 (1.118)
L Avg 0.139 (0.157) 5.38 (1.12) 5.14 (1.06) 24.04 (14.20) 6.80 (2.90) 0.991 (0.048) 5.41 (1.05) 81.75 (10.04)
P-CI 0.030 (0.022) 3.60 (1.818) 4.05 (1.468) 25.542 (15.107) 8.777 (1.362) 0.988 (0.056) 5.75 (0.967)
P-DM 0.061 (0.068) 3.60 (1.875) 4.30 (1.625) 23.833 (14.854) 9.839 (1.649) 1.000 (0.000) 5.50 (0.946)
P-CM 0.045 (0.031) 3.75 (1.943) 4.20 (1.609) 23.267 (15.751) 10.547 (2.108) 1.000 (0.000) 5.60 (0.940)
P-SL 0.040 (0.034) 3.95 (1.820) 4.50 (1.504) 20.983 (13.689) 10.874 (1.905) 0.988 (0.056) 5.90 (0.788)
P Avg 0.044 (0.043) 3.73 (1.84) 4.26 (1.53) 23.41 (14.68) 10.01 (1.92) 0.994 (0.039) 5.69 (0.91) 81.25 (13.32)
V-CI 0.237 (0.164) 3.60 (1.698) 4.35 (1.348) 32.708 (18.492) 16.064 (5.545) 0.718 (0.204) 4.45 (1.731)
V-DM 0.316 (0.216) 3.90 (1.774) 5.10 (1.210) 23.733 (19.321) 9.306 (1.949) 0.968 (0.103) 5.35 (1.348)
V-CM 0.224 (0.147) 3.70 (1.838) 4.35 (1.348) 28.500 (19.812) 13.947 (2.857) 0.887 (0.128) 4.40 (1.875)
V-SL 0.235 (0.229) 4.15 (1.755) 5.00 (1.170) 25.750 (22.488) 17.398 (6.939) 0.802 (0.183) 5.00 (1.686)
V Avg 0.253 (0.192) 3.84 (1.75) 4.70 (1.30) 27.67 (19.99) 14.18 (5.60) 0.844 (0.183) 4.80 (1.69) 70.88 (18.84)

B Study 2

B.1 Measures in study 2

Table 7 and Table 8 show the quality of conversation during study 2. Table 9 shows the subjective ratings on quality of IoT manipulation.

Table 7: Conversation quality ratings by hosts for both IoT and No_IoT conditions.
IoT No_IoT IoT No_IoT IoT No_IoT IoT No_IoT IoT No_IoT IoT No_IoT
Mean 6.417 6.667 5.750 6.667 6.333 6.417 5.833 6.167 6.167 6.250 3.000 4.333
SD 0.669 0.651 0.886 0.651 0.888 0.669 0.718 0.577 0.718 0.622 1.537 1.923
Median 6.500 7.000 6.000 7.000 6.500 6.500 6.000 6.000 6.000 6.000 3.000 5.000
25th percentile 6.000 6.750 5.000 6.750 6.000 6.000 5.000 6.000 6.000 6.000 1.750 2.750
75th percentile 7.000 7.000 6.000 7.000 7.000 7.000 6.000 6.250 7.000 7.000 4.250 6.000
Wilcoxon (p-value) p = 0.074 p = 0.005 p = 0.386 p = 0.065 p = 0.445 p = 0.010
Wilcoxon (Z score) Z = −1.604 Z = −2.521 Z = −0.535 Z = −1.468 Z = −0.270 Z = −2.369
Table 8: Conversation quality ratings by guests for both IoT and No_IoT conditions.
IoT No_IoT IoT No_IoT IoT No_IoT IoT No_IoT IoT No_IoT IoT No_IoT IoT No_IoT
Mean 6.583 6.833 5.583 6.750 5.667 6.750 5.500 5.417 5.667 6.583 6.500 6.583 4.250 4.917
SD 0.669 0.389 0.996 0.622 1.155 0.622 1.314 1.311 1.073 0.793 0.798 0.669 1.712 1.443
Median 7.000 7.000 6.000 7.000 6.000 7.000 6.000 6.000 6.000 7.000 7.000 7.000 4.500 4.500
25th percentile 6.000 7.000 5.000 7.000 5.750 7.000 5.000 5.000 5.000 6.750 6.000 6.000 2.750 4.000
75th percentile 7.000 7.000 6.000 7.000 6.000 7.000 6.000 6.000 6.250 7.000 7.000 7.000 5.250 6.000
Wilcoxon (p-value) p = 0.117 p = 0.006 p = 0.007 p = 0.528 p = 0.031 p = 0.425 p = 0.084
Wilcoxon (Z score) Z = −1.214 Z = −2.497 Z = −2.395 Z = 0.000 Z = −1.886 Z = −0.365 Z = −1.437
Table 9: Quality of IoT manipulation ratings by hosts (H) and guests (G).
[H] Relaxation [H] Politeness [H] Interruption [H] SUS [G] Interruption [G] Politeness [G] Hospitality
Mean 6.000 5.583 2.417 84.583 3.000 5.750 6.583
Std. Deviation 1.279 1.730 1.379 8.450 0.953 1.215 0.669
Median 6.000 6.000 2.000 87.50 3.000 6.000 7.000
25th percentile 6.000 4.750 1.750 79.375 2.00 5.750 6.000
75th percentile 7.000 7.000 3.000 90.000 3.250 6.250 7.000


  • Evan Ackerman. 2021. Bosch Gets Smartglasses Right With Tiny Eyeball Lasers. Retrieved February 06, 2021.
  • Naresh Kumar Agarwal and Wenqing Lu. 2020. Response to non-response: How people react when their smartphone messages and calls are ignored. Proceedings of the Association for Information Science and Technology 57, 1 (2020), e260.
  • Fouad Alallah, Ali Neshati, Yumiko Sakamoto, Khalad Hasan, Edward Lank, Andrea Bunt, and Pourang Irani. 2018. Performer vs. observer: whose comfort level should we consider when examining the social acceptability of input modalities for head-worn display?. In Proceedings of the 24th ACM Symposium on Virtual Reality Software and Technology(VRST ’18). Association for Computing Machinery, New York, NY, USA, 1–9.
  • Amr Alanwar, Moustafa Alzantot, Bo-Jhang Ho, Paul Martin, and Mani Srivastava. 2017. SeleCon: Scalable IoT Device Selection and Control Using Hand Gestures. In Proceedings of the Second International Conference on Internet-of-Things Design and Implementation(Pittsburgh, PA, USA) (IoTDI ’17). Association for Computing Machinery, New York, NY, USA, 47–58.
  • Fraser Anderson, Tovi Grossman, Daniel Wigdor, and George Fitzmaurice. 2015. Supporting Subtlety with Deceptive Devices and Illusory Interactions. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (Seoul, Republic of Korea) (CHI ’15). Association for Computing Machinery, New York, NY, USA, 1489–1498.
  • Michael Argyle and Mark Cook. 1976. Gaze and mutual gaze.(1976).
  • Michael Argyle and Janet Dean. 1965. Eye-contact, distance and affiliation. Sociometry (1965), 289–304.
  • Daniel Ashbrook, Carlos Tejada, Dhwanit Mehta, Anthony Jiminez, Goudam Muralitharam, Sangeeta Gajendra, and Ross Tallents. 2016. Bitey: An Exploration of Tooth Click Gestures for Hands-Free User Interface Control. In Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services (Florence, Italy) (MobileHCI ’16). Association for Computing Machinery, New York, NY, USA, 158–169.
  • Gilles Bailly, Eric Lecolinet, and Laurence Nigay. 2008. Flower Menus: A New Type of Marking Menu with Large Menu Breadth, within Groups and Efficient Expert Mode Memorization. In Proceedings of the Working Conference on Advanced Visual Interfaces (Napoli, Italy) (AVI ’08). Association for Computing Machinery, New York, NY, USA, 15–22.
  • Gilles Bailly, Eric Lecolinet, and Laurence Nigay. 2016. Visual Menu Techniques. ACM Comput. Surv. 49, 4, Article 60 (dec 2016), 41 pages.
  • Aaron Bangor, Philip Kortum, and James Miller. 2009. Determining What Individual SUS Scores Mean: Adding an Adjective Rating Scale. J. Usability Studies 4, 3 (may 2009), 114–123.
  • Ahmet Börütecene, Idil Bostan, Ekin Akyürek, Alpay Sabuncuoglu, Ilker Temuzkusu, Çaglar Genç, Tilbe Göksun, and Oguzhan Özcan. 2018. Through the Glance Mug: A Familiar Artefact to Support Opportunistic Search in Meetings. In Proceedings of the Twelfth International Conference on Tangible, Embedded, and Embodied Interaction (Stockholm, Sweden) (TEI ’18). Association for Computing Machinery, New York, NY, USA, 674–683.
  • Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology 3, 2 (Jan. 2006), 77–101.
  • John Brooke. 1996. SUS - A quick and dirty usability scale. Usability evaluation in industry 189, 194 (1996), 7.
  • Marc Brysbaert. 2019. How many words do we read per minute? A review and meta-analysis of reading rate. Journal of Memory and Language 109 (2019), 104047.
  • J. Callahan, D. Hopkins, M. Weiser, and B. Shneiderman. 1988. An Empirical Comparison of Pie vs. Linear Menus. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Washington, D.C., USA) (CHI ’88). Association for Computing Machinery, New York, NY, USA, 95–100.
  • L Mark Carrier, Larry D Rosen, Nancy A Cheever, and Alex F Lim. 2015. Causes, effects, and practicalities of everyday multitasking. Developmental Review 35(2015), 64–78.
  • Isha Chaturvedi, Farshid Hassani Bijarbooneh, Tristan Braud, and Pan Hui. 2019. Peripheral vision: a new killer app for smart glasses. In Proceedings of the 24th International Conference on Intelligent User Interfaces. 625–636.
  • Varoth Chotpitayasunondh and Karen M. Douglas. 2018. The effects of “phubbing” on social interaction. Journal of Applied Social Psychology 48, 6 (2018), 304–316. _eprint:
  • Enrico Costanza, Samuel A. Inverso, and Rebecca Allen. 2005. Toward Subtle Intimate Interfaces for Mobile Devices Using an EMG Controller. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Portland, Oregon, USA) (CHI ’05). Association for Computing Machinery, New York, NY, USA, 481–489.
  • Enrico Costanza, Samuel A. Inverso, Elan Pavlov, Rebecca Allen, and Pattie Maes. 2006. Eye-q: Eyeglass Peripheral Display for Subtle Intimate Notifications. In Proceedings of the 8th Conference on Human-Computer Interaction with Mobile Devices and Services(Helsinki, Finland) (MobileHCI ’06). Association for Computing Machinery, New York, NY, USA, 211–218.
  • Saverio Debernardis, Michele Fiorentino, Michele Gattullo, Giuseppe Monno, and Antonio Emmanuele Uva. 2014. Text Readability in Head-Worn Displays: Color and Style Optimization in Video versus Optical See-Through Devices. IEEE Transactions on Visualization and Computer Graphics 20, 1 (Jan. 2014), 125–139.
  • Rajib Dey, Sayma Sultana, Afsaneh Razi, and Pamela J. Wisniewski. 2020. Exploring Smart Home Device Use by Airbnb Hosts. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems(CHI EA ’20). Association for Computing Machinery, New York, NY, USA, 1–8.
  • Sarah Diefenbach and Daniel Ullrich. 2019. Disrespectful Technologies: Social Norm Conflicts in Digital Worlds. In Advances in Usability, User Experience and Assistive Technology, Tareq Z. Ahram and Christianne Falcão (Eds.). Springer International Publishing, Cham, 44–56.
  • Jérémie Francone, Gilles Bailly, Eric Lecolinet, Nadine Mandran, and Laurence Nigay. 2010. Wavelet Menus on Handheld Devices: Stacking Metaphor for Novice Mode and Eyes-Free Selection for Expert Mode. In Proceedings of the International Conference on Advanced Visual Interfaces (Roma, Italy) (AVI ’10). Association for Computing Machinery, New York, NY, USA, 173–180.
  • Koumei Fukahori, Daisuke Sakamoto, and Takeo Igarashi. 2015. Exploring Subtle Foot Plantar-Based Gestures with Sock-Placed Pressure Sensors. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems(Seoul, Republic of Korea) (CHI ’15). Association for Computing Machinery, New York, NY, USA, 3019–3028.
  • Google. 2022. Google home - apps on Google Play.
  • Google. 2022. Smart Home Device Traits | Actions on Google Smart Home.
  • Hall and Edward Twitchell. 1966. The hidden dimension. Vol. 609. Anchor.
  • Sandra G. Hart. 2006. Nasa-Task Load Index (NASA-TLX); 20 Years Later. Proceedings of the Human Factors and Ergonomics Society Annual Meeting 50, 9(2006), 904–908.
  • Roy S. Hessels. 2020. How does gaze to faces support face-to-face interaction? A review and perspective. Psychonomic Bulletin & Review 27, 5 (Oct. 2020), 856–881.
  • Yi-Ta Hsieh, Antti Jylhä, Valeria Orso, Luciano Gamberini, and Giulio Jacucci. 2016. Designing a Willing-to-Use-in-Public Hand Gestural Interaction Technique for Smart Glasses. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, California, USA) (CHI ’16). Association for Computing Machinery, New York, NY, USA, 4203–4215.
  • Shamsi T. Iqbal, Jonathan Grudin, and Eric Horvitz. 2011. Peripheral computing during presentations: perspectives on costs and preferences. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems(CHI ’11). Association for Computing Machinery, New York, NY, USA, 891–894.
  • Yuta Itoh, Tobias Langlotz, Jonathan Sutton, and Alexander Plopski. 2021. Towards Indistinguishable Augmented Reality: A Survey on Optical See-through Head-mounted Displays. Comput. Surveys 54, 6 (July 2021), 120:1–120:36.
  • Timo Jakobi, Corinna Ogonowski, Nico Castelli, Gunnar Stevens, and Volker Wulf. 2017. The Catch(Es) with Smart Home: Experiences of a Living Lab Field Study. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 1620–1633.
  • Nuwan Janaka, Chloe Haigh, Hyeongcheol Kim, Shan Zhang, and Shengdong Zhao. 2022. Paracentral and near-peripheral visualizations: Towards attention-maintaining secondary information presentation on OHMDs during in-person social interactions. (2022), 14.
  • Rikke Hagensby Jensen, Yolande Strengers, Jesper Kjeldskov, Larissa Nicholls, and Mikael B. Skov. 2018. Designing the Desirable Smart Home: A Study of Household Experiences and Energy Consumption Impacts. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–14.
  • Steven J. Kerr, Mark D. Rice, Yinquan Teo, Marcus Wan, Yian Ling Cheong, Jamie Ng, Lillian Ng-Thamrin, Thant Thura-Myo, and Dominic Wren. 2011. Wearable Mobile Augmented Reality: Evaluating Outdoor User Experience. In Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry (Hong Kong, China) (VRCAI ’11). Association for Computing Machinery, New York, NY, USA, 209-216.
  • Paul A. Kirschner and Aryn C. Karpinski. 2010. Facebook® and academic performance. Computers in Human Behavior 26, 6 (2010), 1237–1245.
  • Marion Koelle, Swamy Ananthanarayan, and Susanne Boll. 2020. Social Acceptability in HCI: A Survey of Methods, Measures, and Design Strategies. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM, Honolulu HI USA, 1–19.
  • Marion Koelle, Matthias Kranz, and Andreas Möller. 2015. Don't look at me that way!: Understanding User Attitudes Towards Data Glasses Usage. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services. ACM, Copenhagen Denmark, 362–372.
  • Quan Kong, Takuya Maekawa, Taiki Miyanishi, and Takayuki Suyama. 2016. Selecting Home Appliances with Smart Glass Based on Contextual Information. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing(Heidelberg, Germany) (UbiComp ’16). Association for Computing Machinery, New York, NY, USA, 97–108.
  • Nallapaneni Manoj Kumar, Neeraj Kumar Singh, and V. K. Peddiny. 2018. Wearable Smart Glass: Features, Applications, Current Progress and Challenges. In 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT). 577–582.
  • Gordon Kurtenbach and William Buxton. 1993. The Limits of Expert Performance Using Hierarchic Marking Menus. In Proceedings of the INTERACT ’93 and CHI ’93 Conference on Human Factors in Computing Systems (Amsterdam, The Netherlands) (CHI ’93). Association for Computing Machinery, New York, NY, USA, 482–487.
  • Recommended Light Levels. 2020. Recommended light levels (illuminance) for outdoor and indoor venues. The Engineering Toolbox. Recommended Light Levels (2020).
  • Richard Li, Jason Wu, and Thad Starner. 2019. TongueBoard: An Oral Interface for Subtle Input. In Proceedings of the 10th Augmented Human International Conference 2019 (Reims, France) (AH2019). Association for Computing Machinery, New York, NY, USA, Article 1, 9 pages.
  • Ziyang Li and Pei-Luen Patrick Rau. 2021. Talking with an IoT-CA: Effects of the Use of Internet of Things Conversational Agents on Face-to-Face Conversations. Interacting with Computers 33, 3 (10 2021), 238–249. arXiv:
  • Irene Lopatovska, Katrina Rink, Ian Knight, Kieran Raines, Kevin Cosenza, Harriet Williams, Perachya Sorsche, David Hirsch, Qi Li, and Adrianna Martinez. 2019. Talk to me: Exploring user interactions with the Amazon Alexa. Journal of Librarianship and Information Science 51, 4 (2019), 984–997.
  • Feiyu Lu, Shakiba Davari, Lee Lisle, Yuan Li, and Doug A. Bowman. 2020. Glanceable AR: Evaluating Information Access Methods for Head-Worn Augmented Reality. In 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). 930–939.
  • Michal Luria, Guy Hoffman, and Oren Zuckerman. 2017. Comparing social robot, screen and voice interfaces for smart-home control. In Proceedings of the 2017 CHI conference on human factors in computing systems. 580–628.
  • William G. Lycan. 1977. Conversation, politeness, and interruption. Paper in Linguistics 10, 1-2 (1977), 23–53.
  • Kent Lyons. 2003. Everyday Wearable Computer Use: A Case Study of an Expert User. In Human-Computer Interaction with Mobile Devices and Services(Lecture Notes in Computer Science), Luca Chittaro (Ed.). Springer, Berlin, Heidelberg, 61–75.
  • I. Scott MacKenzie. 2013. Human-computer interaction: an empirical research perspective (first edition ed.). Morgan Kaufmann is an imprint of Elsevier, Amsterdam.
  • Ville Mäkelä, Johannes Kleine, Maxine Hood, Florian Alt, and Albrecht Schmidt. 2021. Hidden Interaction Techniques: Concealed Information Acquisition and Texting on Smartphones and Wearables. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 248, 14 pages.
  • Adam M. Mastroianni, Daniel T. Gilbert, Gus Cooney, and Timothy D. Wilson. 2021. Do conversations end when people want them to?Proceedings of the National Academy of Sciences of the United States of America 118, 10 (March 2021), e2011809118.
  • Sven Mayer, Lars Lischke, Paweł W. Woźniak, and Niels Henze. 2018. Evaluating the Disruptiveness of Mobile Interactions: A Mixed-Method Approach. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems - CHI ’18. ACM Press, Montreal QC, Canada, 1–14.
  • Gerard McAtamney and Caroline Parker. [n. d.]. An examination of the effects of a wearable display on informal face-to-face communication. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (New York, NY, USA, 2006-04-22) (CHI ’06). Association for Computing Machinery, 45–54.
  • Tien T. Nguyen, Duyen T. Nguyen, Shamsi T. Iqbal, and Eyal Ofek. 2015. The Known Stranger: Supporting Conversations between Strangers with Personalized Topic Suggestions. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, Seoul Republic of Korea, 555–564.
  • Donald A. Norman. 2013. The design of everyday things(revised and expanded edition ed.). Basic Books.
  • Eyal Ofek, Shamsi T. Iqbal, and Karin Strauss. [n. d.]. Reducing disruption from subtle information delivery during a conversation: mode and bandwidth investigation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (New York, NY, USA, 2013-04-27) (CHI ’13). Association for Computing Machinery, 3111–3120.
  • Julius Panero and Martin Zelnik. 1979. Human dimension & interior space: a source book of design reference standards. Watson-Guptill.
  • Harold Pashler. 1994. Dual-task interference in simple tasks: data and theory.Psychological bulletin 116, 2 (1994), 220.
  • Simon T. Perrault, Eric Lecolinet, Yoann Pascal Bourse, Shengdong Zhao, and Yves Guiard. 2015. Physical Loci: Leveraging Spatial, Object and Semantic Memory for Command Selection. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (Seoul, Republic of Korea) (CHI ’15). Association for Computing Machinery, New York, NY, USA, 299–308.
  • Henning Pohl, Andreea Muresan, and Kasper Hornbæk. 2019. Charting Subtle Interaction in the HCI Literature. Association for Computing Machinery, New York, NY, USA, 1–15.
  • Martin Porcheron, Joel E. Fischer, Stuart Reeves, and Sarah Sharples. 2018. Voice Interfaces in Everyday Life. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI ’18). Association for Computing Machinery, New York, NY, USA, 1–12.
  • Aung Pyae and Tapani N. Joelsson. 2018. Investigating the Usability and User Experiences of Voice User Interface: A Case of Google Home Smart Speaker. In Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct(Barcelona, Spain) (MobileHCI ’18). Association for Computing Machinery, New York, NY, USA, 127–131.
  • Mikko J. Rissanen, Samantha Vu, Owen Noel Newton Fernando, Natalie Pang, and Schubert Foo. 2013. Subtle, Natural and Socially Acceptable Interaction Techniques for Ringterfaces — Finger-Ring Shaped User Interfaces. In Distributed, Ambient, and Pervasive Interactions, Norbert Streitz and Constantine Stephanidis (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 52–61.
  • Radiah Rivu, Yasmeen Abdrabou, Ken Pfeuffer, Augusto Esteves, Stefanie Meitner, and Florian Alt. 2020. StARe: Gaze-Assisted Face-to-Face Communication in Augmented Reality. In Symposium on Eye Tracking Research and Applications. ACM, Stuttgart Germany, 1–5. ZSCC: 0000013.
  • Rufat Rzayev, Susanne Korbely, Milena Maul, Alina Schark, Valentin Schwind, and Niels Henze. 2020. Effects of Position and Alignment of Notifications on AR Glasses during Social Interaction. Association for Computing Machinery, New York, NY, USA.
  • Krystian Samp and Stefan Decker. 2010. Supporting Menu Design with Radial Layouts. In Proceedings of the International Conference on Advanced Visual Interfaces (Roma, Italy) (AVI ’10). Association for Computing Machinery, New York, NY, USA, 155–162.
  • Shardul Sapkota, Ashwin Ram, and Shengdong Zhao. 2021. Ubiquitous Interactions for Heads-Up Computing: Understanding Users’ Preferences for Subtle Interaction Techniques in Everyday Settings. In Proceedings of the 23rd International Conference on Mobile Human-Computer Interaction(MobileHCI ’21). Association for Computing Machinery, New York, NY, USA, Article 36, 15 pages.
  • B. Schilit, N. Adams, and R. Want. 1994. Context-Aware Computing Applications. In 1994 First Workshop on Mobile Computing Systems and Applications. 85–90.
  • Alex Sciuto, Arnita Saini, Jodi Forlizzi, and Jason I. Hong. 2018. "Hey Alexa, What's Up?": A Mixed-Methods Studies of In-Home Conversational Agent Usage. In Proceedings of the 2018 Designing Interactive Systems Conference (Hong Kong, China) (DIS ’18). Association for Computing Machinery, New York, NY, USA, 857–868.
  • Ludwig Sidenmark, Dominic Potts, Bill Bapisch, and Hans Gellersen. 2021. Radi-Eye: Hands-Free Radial Interfaces for 3D Interaction Using Gaze-Activated Head-Crossing. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 740, 11 pages.
  • Norman Makoto Su and Lulu Wang. 2015. From Third to Surveilled Place: The Mobile in Irish Pubs. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (Seoul, Republic of Korea) (CHI ’15). Association for Computing Machinery, New York, NY, USA, 1659–1668.
  • Charles Tijus, Javier Barcenilla, Brigitte Cambon de Lavalette, and Jean-Guy Meunier. 2007. The Design, Understanding and Usage of Pictograms. Written Documents in the Workplace (Jan. 2007), 17–31.
  • Ying-Chao Tung, Chun-Yen Hsu, Han-Yu Wang, Silvia Chyou, Jhe-Wei Lin, Pei-Jung Wu, Andries Valstar, and Mike Y. Chen. 2015. User-Defined Game Input for Smart Glasses in Public Space. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems - CHI ’15. ACM Press, Seoul, Republic of Korea.
  • Mariek M.P. Vanden Abeele, Marjolijn L. Antheunis, and Alexander P. Schouten. 2016. The effect of mobile messaging during a conversation on impression formation and interaction quality. Computers in Human Behavior 62 (2016), 562–569.
  • Radu-Daniel Vatavu and Jean Vanderdonckt. 2020. Design Space and Users’ Preferences for Smartglasses Graphical Menus: A Vignette Study. In 19th International Conference on Mobile and Ubiquitous Multimedia (Essen, Germany) (MUM 2020). Association for Computing Machinery, New York, NY, USA, 1–12.
  • Martin Weigel and Jürgen Steimle. 2017. DeformWear: Deformation Input on Tiny Wearable Devices. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 2 (June 2017), 28:1–28:23.
  • Kristin Williams, Karyn Moffatt, Denise McCall, and Leah Findlater. 2015. Designing Conversation Cues on a Head-Worn Display to Support Persons with Aphasia. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, Seoul Republic of Korea, 231–240.
  • Jacob O. Wobbrock, Leah Findlater, Darren Gergle, and James J. Higgins. 2011. The aligned rank transform for nonparametric factorial analyses using only anova procedures. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems(CHI ’11). Association for Computing Machinery, New York, NY, USA, 143–146.
  • Shengdong Zhao, Pierre Dragicevic, Mark Chignell, Ravin Balakrishnan, and Patrick Baudisch. 2007. Earpod: Eyes-Free Menu Selection Using Touch Input and Reactive Audio Feedback. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (San Jose, California, USA) (CHI ’07). Association for Computing Machinery, New York, NY, USA, 1395–1404.










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