Large language models (LLMs) chatbots like ChatGPT are increasingly used for mental health support. They offer accessible, therapeutic support but also raise concerns about misinformation, over-reliance, and risks in high-stakes contexts of mental health. We crowdsource large-scale users' posts from six major social media platforms to examine how people discuss their interactions with LLM chatbots across different mental health conditions. Through an LLM-assisted pipeline grounded in Value-Sensitive Design (VSD), we mapped the relationships across user-reported sentiments, mental health conditions, perspectives, and values. Our results reveal that the use of LLM chatbots is condition-specific. Users with neurodivergent conditions (e.g., ADHD, ASD) report strong positive sentiments and instrumental or appraisal support, whereas higher-risk disorders (e.g., schizophrenia, bipolar disorder) show more negative sentiments. We further uncover how user perspectives co-occur with underlying values, such as identity, autonomy, and privacy. Finally, we discuss shifting from "one-size-fits-all" chatbot design toward condition-specific, value-sensitive LLM design.

Improving accuracy, time spent, and ubiquity of delineation has been a long-standing design aim, yet many HCI works have overlooked high-stakes and complex healthcare annotation. We explore contouring, a critical workflow aimed at identifying and segmenting tumors, usually performed on immobile desktop computers in clinics, in which limited support for mobile access leads to prolonged and subpar treatment planning. Following interviews and think-aloud studies (N = 10 physicians), we report key contouring behaviors, and later design a novel cross-device prototype that enables contouring on everyday touch devices. We compared contouring via desktop and touch in a lab study (N = 8 residents) and found that mobile phones not only yielded similar accuracy, but also took significantly less time. Our results point to three broad design guidelines for cross-device solutions deployed within standalone healthcare workflows, and highlight how incorporating different device and input modalities can improve treatment delivery in today’s distributed healthcare environments.

Apprenticeship is the predominant method for transferring specialized medical skills, yet the inter-dynamics between faculty and residents, including methods of feedback exchange are under-explored. We specifically investigate contouring: outlining tumors in preparation for radiotherapy, a critical skill that when performed subpar, severely degrades patient survival. Interviews and design-thinking workshops (N = four faculty; six residents) revealed misalignment between teaching methods and residents who desired timely, relevant, and diverse feedback. We further discuss reasons: overlapping learning content and strategies to ease tensions between clinical and teaching duties, and lack of support for exchange of cognitive processes. The follow-up survey study (N = 67 practitioners from 31 countries), which contained annotation and sketching tasks, provided diverse perspective over effective feedback elements. We lastly present sociotechnical implications in supporting faculty’s teaching duties and learners’ cognitive models, such as systematically leveraging senior learners in providing case-based guidance and supporting double-sided flow of cognitive information via in-situ video snippets.

Providing asynchronous feedback is a critical step in the 3D design workflow. A common approach to providing feedback is to pair textual comments with companion reference images, which helps illustrate the gist of text. Ideally, feedback providers should possess 3D and image editing skills to create reference images that can effectively describe what they have in mind. However, they often lack such skills, so they have to resort to sketches or online images that might not match well with the current 3D design. To address this, we introduce MemoVis , a text editor interface that assists feedback providers in creating reference images with generative AI driven by the feedback comments. First, a novel real-time viewpoint suggestion feature, based on a vision-language foundation model, helps feedback providers anchor a comment with a camera viewpoint. Second, given a camera viewpoint, we introduce three types of image modifiers based on pre-trained 2D generative models to turn a text comment into an updated version of the 3D scene from that viewpoint. We conducted a within-subjects study with \(14\) feedback providers, demonstrating the effectiveness of MemoVis. The quality and explicitness of the companion images were evaluated by another eight participants with prior 3D design experience.

While voice user interfaces offer increased accessibility due to hands-free and eyes-free interactions, older adults often have challenges such as constructing structured requests and perceiving how such devices operate. Voice-first user interfaces have the potential to address these challenges by enabling multimodal interactions. Standalone voice + touchscreen Voice Assistants (VAs), such as Echo Show, are specific types of devices that adopt such interfaces and are gaining popularity. However, the affordances of the additional touchscreen for older adults are unknown. Through a 40-day real-world deployment with older adults living independently, we present a within-subjects study (N = 16; age M = 82.5, SD = 7.77, min. = 70, max. = 97) to understand how a built-in touchscreen might benefit older adults during device setup, conducting self-report diary survey, and general uses. We found that while participants appreciated the visual outputs, they still preferred to respond via speech instead of touch. We identified six design implications that can inform future innovations of senior-friendly VAs for managing healthcare and improving quality of life.

Acquiring accessibility information about unfamiliar places in advance is essential for wheelchair users to make better decisions about physical visits. Today’s assessment approaches such as phone calls, photos/videos, or 360° virtual tours often fall short of providing the specific accessibility details needed for individual differences. For example, they may not reveal crucial information like whether the legroom underneath a table is spacious enough or if the spatial configuration of an appliance is convenient for wheelchair users. In response, we present Embodied Exploration, a Virtual Reality (VR) technique to deliver the experience of a physical visit while keeping the convenience of remote assessment. Embodied Exploration allows wheelchair users to explore high-fidelity digital replicas of physical environments with themselves embodied by avatars, leveraging the increasingly affordable VR headsets. With a preliminary exploratory study, we investigated the needs and iteratively refined our techniques. Through a real-world user study with six wheelchair users, we found Embodied Exploration is able to facilitate remote and accurate accessibility assessment. We also discuss design implications for embodiment, safety, and practicality.

While paper instructions are a mainstream medium for sharing knowledge, consuming such instructions and translating them into activities can be inefficient due to the lack of connectivity with the physical environment. We propose PaperToPlace, a novel workflow comprising an authoring pipeline, which allows the authors to rapidly transform and spatialize existing paper instructions into an MR experience, and a consumption pipeline, which computationally places each instruction step at an optimal location that is easy to read and does not occlude key interaction areas. Our evaluation of the authoring pipeline with 12 participants demonstrates the usability of our workflow and the effectiveness of using a machine learning based approach to help extract the spatial locations associated with each step. A second within-subjects study with another 12 participants demonstrates the merits of our consumption pipeline to reduce context-switching effort by delivering individual segmented instruction steps and offering hands-free affordances.

Population aging is an increasingly important consideration for health care in the 21th century, and continuing to have access and interact with digital health information is a key challenge for aging populations. Voice-based Intelligent Virtual Assistants (IVAs) are promising to improve the Quality of Life (QoL) of older adults, and coupled with Ecological Momentary Assessments (EMA) they can be effective to collect important health information from older adults, especially when it comes to repeated time-based events. However, this same EMA data is hard to access for the older adult: although the newest IVAs are equipped with a display, the effectiveness of visualizing time-series based EMA data on standalone IVAs has not been explored. To investigate the potential opportunities for visualizing time-series based EMA data on standalone IVAs, we designed a prototype system, where older adults are able to query and examine the time-series EMA data on Amazon Echo Show - a widely used commercially available standalone screen-based IVA. We conducted a preliminary semi-structured interview with a geriatrician and an older adult, and identified three findings that should be carefully considered when designing such visualizations.

Contouring is an indispensable step in Radiotherapy (RT) treatment planning. However, today’s contouring software is constrained to only work with a 2D display, which is less intuitive and requires high task loads. Virtual Reality (VR) has shown great potential in various specialties of healthcare and health sciences education due to the unique advantages of intuitive and natural interactions in immersive spaces. VR-based radiation oncology integration has also been advocated as a target healthcare application, allowing providers to directly interact with 3D medical structures. We present VRContour and investigate how to effectively bring contouring for radiation oncology into VR. Through an autobiographical iterative design, we defined three design spaces focused on contouring in VR with the support of a tracked tablet and VR stylus, and investigating dimensionality for information consumption and input (either 2D or 2D + 3D). Through a within-subject study (n = 8), we found that visualizations of 3D medical structures significantly increase precision, and reduce mental load, frustration, as well as overall contouring effort. Participants also agreed with the benefits of using such metaphors for learning purposes.

Accurately drawing non-planar 3D curves in immersive Virtual Reality (VR) is indispensable for many precise 3D tasks. However, due to lack of physical support, limited depth perception, and the non-planar nature of 3D curves, it is challenging to adjust mid-air strokes to achieve high precision. Instead of creating new interaction techniques, we investigated how task geometric shapes and input modalities affect precision-first drawing performance in a within-subject study (n=12) focusing on 3D target tracing in commercially available VR headsets. We found that compared to using bare hands, VR controllers and pens yield nearly 30% of precision gain, and that the tasks with large curvature, forward-backward or left-right orientations perform best. We finally discuss opportunities for designing novel interaction techniques for precise 3D drawing. We believe that our work will benefit future research aiming to create usable toolboxes for precise 3D drawing.

Voice-based Intelligent Virtual Assistants (IVAs) promise to improve healthcare management and Quality of Life (QOL) by introducing the paradigm of hands-free and eye-free interactions. However, there has been little understanding regarding the challenges for designing such systems for older adults, especially when it comes to healthcare related tasks. To tackle this, we consider the processes of care delivery and QOL enhancements for older adults as a collaborative task between patients and providers. By interviewing 16 older adults living independently or semi-independently and 5 providers, we identified 12 barriers that older adults might encounter during daily routine and while managing health. We ultimately highlighted key design challenges and opportunities that might be introduced when integrating voice-based IVAs into the life of older adults. Our work will benefit practitioners who study and attempt to create full-fledged IVA-powered smart devices to deliver better care and support an increased QOL for aging populations.

Immersive face-to-virtual-face telecommunication is one unique use case for virtual reality (VR) technologies. Existing camera-based telephony systems cannot be used for such immersive VR video chat, due to the physical occlusions of head-mounted displays (HMDs) and/or unwieldy positioning of cameras. To address these, we present ExGSense, a new VR input modality that can sense and reconstruct both upper and lower facial gestures, by only using lightweight biopotential sensors embedded within the HMDs. We optimize the sensor arrangement based on facial anatomy and employ a multiview classification pipeline to exploit the multiple dimensions of signal features. We thus enable ExGSense to detect whole facial gestures by using a sparse set of biopotential transducers. We prototyped ExGSense and evaluated its performance with 42 facial gestures and across different users. We showed a 93% accuracy for user-specific evaluation, and 77% accuracy for user-independent evaluation with low calibration overhead. We believe ExGSense constitutes a promising input modality for immersive VR interactions.

Many human activities involve interactions with passive objects. By wirelessly sensing human interactions with such "things", one can infer activities at a fine resolution, enabling a new wave of ubiquitous applications. This forms the basis of the tangible user interface allowing individual to use omnipresent objects as a control interface to the digital world. Existing works have tendencies to create such interface with complicated circuitry, leading to overwhelm complexities. To conquer these, we propose the inkjet printable capacitive tags (CapTags), empowering a new paradigm of printable communications and sensing modality. We use discrete capacitive and inductive components to simulate the tag-interrogator system, and prove the feasibility of proposed hardware featurization and high frequency sweeping strategy where the information can be encoded in the resonating spikes. This enables the touch points to be detected by searching resonating detune effects. Although this work only includes the designs and simulations, we believe this new sensing modality would truly realize the vision of printable ubiquitous computing.

Voice assistants (VAs) are becoming highly popular recently as a general means of interacting with the Internet of Things. However, the use of always-on microphones on VAs imposes a looming threat on users' privacy. In this paper, we propose MicShield, the first system that serves as a companion device to enforce privacy preservation on VAs. MicShield introduces a novel selective jamming mechanism, which obfuscates the user's private speech while passing legitimate voice commands to the VAs. It achieves this by using a phoneme level jamming control pipeline. Our implementation and experiments demonstrate that MicShield can effectively protect a user's private speech, without affecting the VA's responsiveness.