Gait recognition enables proactive and personalized smart home interactions, but its long-term reliability is challenged by the non-static nature of gait. Covariates like carrying items and clothing induce a persistent domain shift that degrades traditional, static models. To solve this, we introduce FlowGait, a mmWave-based framework designed for robust, long-term adaptation. It combines self-training with continual learning, allowing the model to daily align with a user's evolving gait by learning from readily available unlabeled data. It features a specialized transformer network for radar spectrogram analysis and a novel two-stage labeling algorithm that leverages the gait's hierarchical nature to assign pseudo-labels to the unlabeled data accurately. Evaluated on three challenging datasets from 47 volunteers (covering 12 gait-covariates, 11 routes, and two weeks), FlowGait achieves high accuracies of 94.8% (cross-covariate), 98.6% (cross-route), and 95.5% (cross-day). Notably, for the long-term dataset, it reduced performance decay from 13.6% to just 1.4%, demonstrating its real-world robustness.
Wheelchair users often face significant barriers to maintaining and adapting their chairs, from resource constraints to limited access to professional services. In response, many turn to social media platforms such as YouTube to share and learn practical knowledge. However, little is known about how wheelchair users document and exchange repair, maintenance, and customization practices online. To address this gap, we analyzed 290 YouTube videos alongside 800 sampled comments using thematic coding and statistical analysis. Our findings revealed diverse user needs, from enhancing mobility to expressing identity, which were addressed through a spectrum of DIY acts, such as accessorizing, bricolage, and major modifications. Engagement analysis further reveals how styling videos attract a broad audience, while custom-built “chair tours” become hubs of knowledge and solidarity. We reflect on using YouTube as a research source and call for a design approach grounded in solidarity that supports the full spectrum of DIY practices.
Autonomy and independent navigation are vital to daily life but remain challenging for individuals with blindness. Robotic systems can enhance mobility and confidence by providing intelligent navigation assistance. However, fully autonomous systems may reduce users’ sense of control, even when they wish to remain actively involved. Although collaboration between user and robot has been recognized as important, little is known about how perceptions of this relationship change with repeated use. We present a repeated exposure study with six blind participants who interacted with a navigation-assistive robot in a real-world museum. Participants completed tasks such as navigating crowds, approaching lines, and encountering obstacles. Findings show that participants refined their strategies over time, developing clearer preferences about when to rely on the robot versus act independently. This work provides insights into how strategies and preferences evolve with repeated interaction and offers design implications for robots that adapt to user needs over time.
Over 100 million retired women in China engage in dance, but their performances are constrained by limited resources and age-related decline. While interactive dance technologies can enhance artistic expression, existing systems are largely inaccessible to non-professional older dancers. This paper explores how interactive dance technologies can be designed with an age-sensitive approach to support retired women in enhancing their stage performance. We conducted two workshops with community-based retired women dancers, employing interactive dance and LLM-powered video generation probes in co-design activities. Findings indicate that age-sensitive adaptations, such as low-barrier keyword input, motion-aligned visual effects, and participatory scaffolds, lowered technical barriers and fostered a sense of authorship. These features enabled retired women to empower their stage, transitioning from passive recipients of stage design to empowered co-creators of performance. We outline design implications for incorporating interactive dance and artificial intelligence-generated content (AIGC) into the cultural practices of retired women, offering broader strategies for age-sensitive creative technologies.
Parkinson’s disease (PD) commonly leads to gait disorders that necessitate long-term rehabilitation dependent on specialists and clinic-based interventions. To reduce dependence on clinicians and investigate how wearable technology can provide continuous guidance for rehabilitation training. We distilled key design principles from patient–clinician interviews and co-designed a gait training system. The system employs inertial measurement units (IMUs) to capture kinematic data, then delivers multimodal cueing (visual, auditory, and somatosensory) aligned with walking features. Two user studies (N = 16 PD patients) evaluated the effectiveness of multimodal cueing, examining strategies for information delivery and gait correction. Results indicated that visual and auditory cueing were more effective for process-oriented adjustments, whereas somatosensory stimulation better supported periodic cueing. Moreover, a dissociation between performance outcomes and user preferences was observed. These findings highlight the potential of wearable technology to provide continuous, daily training guidance for PD patients.
Adherence to physical activity is critical for healthy ageing, yet older adults often struggle to maintain consistent routines. We introduce Group Activity Metrics (GAMs), a design construct comprising group visualisations shared within familiar, small social groups, and examine how they facilitate social interaction and support walking adherence among Indian older adults. Guided by Social Cognitive Theory (SCT) and informed by interviews and focus groups with 22 participants, we designed GAMs and deployed them in two walking cohorts. We evaluated them through social prototyping, an approach that captures early-stage social responses to design ideas. Our findings show that GAMs sparked curiosity and enabled social interaction, suggesting potential to support walking adherence. This work contributes empirical insights into the role of small-group social interaction in walking adherence, introduces GAMs as a theory-informed design construct, and positions social prototyping as a methodological approach for evaluating socially driven technologies in HCI.
Social robots in the home bring new privacy risks and concerns for older adults. Yet, current technology privacy mechanisms typically use a one-time and universal consent mechanism (e.g., user agreement checkbox, browser cookie setting, etc.), lacking consideration of how privacy is holistically experienced. Designing for privacy requires a multidimensional approach to support how older adults experience privacy. To investigate older adult-centered privacy mechanisms for social robots, we conducted two participatory design (PD) workshops at local assisted living facilities. Our findings from these workshops suggest that older adults do not treat privacy as static, but as a temporal and situational practice that requires continuous negotiations and revisions. We subsequently conducted a post-PD speculative design (SD) process that extracted three design features for privacy—aware social robots-privacy profiles, real-time privacy feedback, and data ownership tools—that can support older adults’ multidimensional privacy experiences.