Figure 1: With A11yShape, (A) a blind or low-vision (BLV) user can create, interpret, and verify 3-D models through (B) a user interface composed of three parts: Code Editor Panel, AI Assistant Panel, and Model Panel.These panels are linked by a cross-representation highlighting mechanism that connects code, textual descriptions, hierarchical model abstractions, and 3-D visual renderings.The system supports the creation of (C) diverse, customized 3-D models created by BLV users.

Although remote learning is widely used for delivering and capturing knowledge, it has limitations in teaching hands-on skills that require nuanced instructions and demonstrations of precise actions, such as massage. Furthermore, scheduling conflicts between instructors and learners often limit the availability of real-time feedback, reducing learning efficiency. To address these challenges, we developed a synthesis tool utilizing an LLM-powered Virtual Teaching Assistant (VTA). This tool integrates multimodal instructions that convey precise data, such as stroke patterns and pressure control, while providing real-time feedback for learners and summarizing their performance for instructors. Our case study with instructors and learners demonstrated the effectiveness of these multimodal instructions and the VTA in enhancing massage teaching and learning. We then discuss the tools' use in other hands-on skills instruction and cognitive process differences in various courses.

Charts are crucial in conveying information across various fields but are inaccessible to blind and low vision (BLV) people without assistive technology. Chart comprehension tools leveraging haptic feedback have been used widely but are often bulky, expensive, and static, rendering them inefficient for conveying chart data. To increase device portability, enable multitasking, and provide efficient assistance in chart comprehension, we introduce a novel system that delivers unobtrusive modulated electrotactile feedback directly to the fingertip edge. Our three-part study with twelve participants confirmed the effectiveness of this system, demonstrating that electrotactile feedback, when applied for 0.5 seconds with a 0.12-second interval, provides the most accurate position and direction recognition. Furthermore, our electrotactile device has proven valuable in assisting BLV participants in comprehending four commonly used charts: line charts, scatterplots, bar charts, and pie charts. We also delve into the implications of our findings on recognition enhancement, presentation modes, and function synergy.

Recent research proposed gaze-assisted gestures to enhance interaction within virtual reality (VR), providing opportunities for people with motor impairments to experience VR. Compared to people with other motor impairments, those with Spinal Muscular Atrophy (SMA) exhibit enhanced distal limb mobility, providing them with more design space. However, it remains unknown what gaze-assisted upper-body gestures people with SMA would want and be able to perform. We conducted an elicitation study in which 12 VR-experienced people with SMA designed upper-body gestures for 26 VR commands, and collected 312 user-defined gestures. Participants predominantly favored creating gestures with their hands. The type of tasks and participants’ abilities influence their choice of body parts for gesture design. Participants tended to enhance their body involvement and preferred gestures that required minimal physical effort, and were aesthetically pleasing. Our research will contribute to creating better gesture-based input methods for people with motor impairments to interact with VR.

Falling is characterized by high incidence and great harm among the elderly. Timely assessing falling risk in daily life is helpful for reducing the occurrence of severe health outcomes. Establishing dataset for falling risk assessment based on wearable devices in the elderly is important work. However, current existing datasets might not reflect the natural gait of the subject due to the discomfort in wearing. Relevant data processing methods based on these datasets have limited practicability and might not be applied to real scenes in daily life. To make daily falling risk assessment possible, we proposed a novel approach to set up a continuous and wearable plantar pressure dataset of 48 older adults along with falling risk labels. The dataset was collected by plantar pressure monitoring shoes which were suitable for daily living spaces. Moreover, the Conv-LSTM algorithm was applied on the dataset, and the average classification result was up to 95.57%, reflecting the effectiveness of this dataset. The dataset is helpful for the studies of falling risk assessment and health monitoring among the elderly.