The lack of social acceptability for wearable devices such as orthopedic foot orthoses can lead to irregular usage and missed health benefits, as shown in prior studies. While AI-generated designs have been explored for prototyping aesthetic hand orthoses, their impact on social acceptability, particularly for foot orthoses, remains unknown. The current state of research is limited, as no empirical evidence exists on whether AI-designed orthoses influence acceptance, nor has the role of customized generative pre-trained transformers (GPTs) and specific prompting strategies been examined in this context. To address these gaps, we conducted two mixed-methods studies to investigate (1) the impact of AI-generated orthosis designs on social acceptability compared to existing orthopedic products and development concepts and (2) how a customized GPT and different prompt keywords influence acceptance. Our results show that AI-generated designs significantly enhance social acceptance across orthotic categories. Furthermore, we found that personalized GPTs and targeted prompt keywords significantly influence user perception. Overall, our findings highlight the potential of using AI to create socially acceptable design solutions for wearable technology and offer new applications for future smart devices. We contribute to generative AI in product design and provide concrete recommendations for optimizing prompting strategies to enhance social acceptance.

Balance board training is a promising method to enhance physical rehabilitation for humans with motor problems through interactive exercises. Previous work highlighted the benefits of balance board training in virtual reality (VR) compared to conventional methods. However, it is still unclear how visual target feedback and the presence of an avatar influence balance behavior in immersive environments. We conducted an experimental user study with 24 participants without motor impairments to investigate the effects of visual target feedback and a human avatar on balance performance and perceived workload in VR. Quantitative results show that visual target feedback significantly improves balance performance without increasing workload in VR. In contrast, an avatar shows no effect on performance and workload, which is also confirmed by qualitative feedback. Finally, we discuss the implications of our study for future developments of virtual balance board training exercises and highlight potential applications of visual target feedback.

Elderly people often face the risk of falls, which increases the need for walking aids to ensure mobility and safety. The occurrence of further injuries requires the use of additional orthopedic aids (e.g., foot orthoses). Wearing such an orthopedic aid also impairs the natural gait pattern, which leads to increased instability in older patients. This highlights the need for innovative solutions for assistive smart walking aids. Doan et al., for example, presented a smart walking aid for fall detection that can detect falls based on machine learning with an accuracy of 99.62% (Doan et al., 2024). This development can lead to improvements in home care and remote health monitoring technologies. However, a solution that allows the combined use of an intelligent walking aid with a smart foot orthosis to provide patients with active feedback does not exist. Based on the identified research gap, we propose the technical development of an augmented walking aid with haptic feedback that enables direct communication of sensor data with a smart foot orthosis. Providing additional feedback in case of problems with the foot orthosis is particularly important for patients with neuropathy in the feet (such as diabetic foot syndrome), for which neural feedback is not possible. Our approach enables direct haptic feedback through vibrations on the handle of the walking aid when acute problems are detected by the smart foot orthosis. The system achieves this by continuously monitoring relevant gait parameters and pressure distributions in the foot orthosis. This should support the proactive adaptation of gait problems and increase safety when using assistive technologies for older people.

Increasing research on intelligent wearable technologies, such as smart insoles for mobile health (mHealth) monitoring, brings new challenges for user-centered design, particularly in data visualization. Research shows various developments in mobile apps for monitoring foot health with smart insoles, while emerging trends like chatbot interactions and improved analytic visualizations offer new opportunities to enhance user experience. However, the usability of various health data visualizations remains unvalidated. A mixed-method experimental user study with 30 participants was conducted to assess the usability of three prototype mHealth applications for smart insoles: Analytical, Basic, and Chatbot visualizations. Quantitative results showed that Basic visualization achieved the highest usability, followed by Analytical, and finally Chatbot. Qualitative feedback supported these findings but also highlighted the potential of Chatbot interactions to enhance data understanding in mHealth apps. We discuss implications for future mHealth applications to monitor foot health and propose design recommendations to improve usability in chatbot interactions.