We examine how neurodivergent individuals experience creating, interacting with, and reflecting on personal data about masking. Although self-tracking is often framed as enabling self-insight, this is rarely our experience as neurodivergent individuals and researchers. To better understand this disconnect, we conducted a two-phase qualitative study. First, a workshop where six participants with autism and/or ADHD crafted visual representations of masking experiences. Then, three participants continued by designing and using personalized self-tracking focused on unmasking over two weeks. Using reflexive thematic analysis of activities and interviews, we find that self-tracking imposes substantial interpretive and emotional demands, shaped by context-dependencies that challenge assumptions in self-tracking. We also find that facilitated sharing of experiences might validate emotional responses and support reflection. We identify three emotional dimensions that shape engagement with personal data in a working model of emotion in self-tracking, and discuss implications for designing self-tracking and reflective practices that incorporate peer support and better account for context and emotional labor.
Mirror Therapy (MT) effectively supports post-stroke upper-limb rehabilitation but requires professional supervision and often leads to low patient motivation. While digital MT systems address these limitations, they typically compromise the embodiment benefits of traditional mirrors. Through informant-driven design with interdisciplinary experts, we developed A-MIrror, an augmented reality mirror system that preserves the view of both mirrored hand and overlays digital guidance. Using dual cameras for eye and hand tracking, the system creates a visual illusion where both real and mirrored hands appear to naturally interact with virtual 3D objects on screen. Our evaluation with 14 post-stroke patients and 8 therapists demonstrates that A-MIrror significantly enhances motivation compared to traditional MT (p < .01) while achieving comparable embodiment experiences and even superior illusion latency (30.47% faster, p < .001) to traditional mirrors. This study presents a promising approach for independent post-stroke rehabilitation that integrates the strengths of both traditional and digital MT, offering insights for enhancing future digital rehabilitation applications.
Vision health is a critical domain of clinical practice, yet global access to diagnosis, rehabilitation, and education remains uneven due to economic and geographic disparities. Extended reality (XR) offers opportunities to extend clinical services through portable assessments and interactive simulations, but its design for vision health has been constrained by technical and translational challenges. This paper presents three studies within a sustained co-design process examining how immersive rendering tools can support vision care across clinical and educational contexts. First, we worked with clinicians to map rendering attributes to clinically meaningful functions, identifying opportunities for diagnostic and training use. Second, we evaluated a prototype visual field assessment, demonstrating feasibility in a diagnostic setting. Third, we applied vision simulations in orthoptic training, enhancing empathy and understanding in an educational setting. Together, these studies show how engaging domain experts advances XR tools for vision health, informing diagnosis, patient–clinician communication, and professional education.
Poor posture during desk-based learning activities can lead to many health issues, such as spinal problems, musculoskeletal discomfort, and myopia. While traditional posture correction systems use immediate feedback to notify users of their wrong posture, they often disrupt users' concentration. This study explores an ambient projection notification system for non-intrusive posture correction notifications during desk-based learning scenarios. Through a notification elicitation study and an expert co-design workshop, we investigated users' perception towards basic elements of projection notification and derived a design space for desk-based projection notification. We then implemented and evaluated PosProjector, an ambient projection notification system, by applying two notification strategies that embody the most representative dimensions of the design space. Results showed that PosProjector can improve users' posture with little task interference and support various media, including paper and tablets. We further discussed the implications of how to design the least intrusive projection notification system for posture correction.
While prior research has focused on providers, caregivers, and adult patients, little is known about adolescents’ perceptions of AI in health learning and management. Utilizing design fiction and co-design methods, we conducted seven workshops with 23 adolescents (aged 14-17) to understand how they anticipate using health AI in the context of a family celiac diagnosis. Our findings reveal that adolescents have four main envisioned roles of health AI: enhancing health understanding and help-seeking, reducing cognitive burden, supporting family health management, and providing guidance while respecting their autonomy. We also identified nuanced trust and a divided view toward emotional support from health AI. These findings suggest that adolescents perceive AI's value as a tool that moves them from efficiency to meaning–one that creates time for valued activities. We discuss opportunities for future health AI systems to be designed to encourage adolescent autonomy and reflection, while also supporting meaningful, dialectical activities.
People diagnosed with a disorder of consciousness (DoC)—a spectrum of consciousness including comatose state—typically have a negative prognosis and their treatment options are limited. We interviewed seven clinical care providers who are involved in caring for DoC patients to explore the potential for technology to facilitate treatment and care of DoC patients. The interviews revealed multiple rich opportunities in this space centered around optimizing sensory stimulation—the systematic delivery of sights, sounds, smells, tastes, and touch. Simultaneously, the interviews also revealed many ethical and technical challenges inherent in DoC patient care, including difficulty in obtaining familiar media to use in the delivery of the sensory stimulation. Our findings underscore the need for technology that complements, not replaces, the human elements of DoC care and healthcare more generally.
Does human–AI assistance unfold in the same way as human–human assistance? This research explores what can be learned from the expertise of blind individuals and sighted volunteers to inform the design of multimodal voice agents and address the enduring challenge of proactivity. Drawing on granular analysis of two representative fragments from a larger corpus, we contrast the practices co-produced by an experienced human remote sighted assistant and a blind participant—as they collaborate to find a stain on a blanket over the phone—with those achieved when the same participant worked with a multimodal voice agent on the same task, a few moments earlier. This comparison enables us to specify precisely which fundamental proactive practices the agent did not enact in situ. We conclude that, so long as multimodal voice agents cannot produce environmentally occasioned vision-based actions, they will lack a key resource relied upon by human remote sighted assistants.