Planning and organizing user studies can be a challenge for novices and student researchers in human-computer interaction (HCI). We present a web-based study planning toolkit for researchers to support their planning and organization with human subjects. The free and open toolkit contains multiple interactive research applications for (1) the selection of quantitative and qualitative research methods in HCI, (2) automated study design planning for experimental user studies, (3) an automated informed consent generation for studies with human subjects, (4) calendar booking for experimental user studies in shared laboratories, and (5) automated participation confirmation certificates. The toolkit can be helpful for Ph.D. students in supervising novices and undergraduates who need support with their planning and study design choices. The toolkit was actively used over two semesters in multiple HCI lectures and formatively evaluated. We contribute with the whole toolkit and encourage other researchers to contribute and further improve the content.

Smart walking aids represent a growing trend in assistive technologies designed to support individuals with mobility impairments in their daily lives and rehabilitation. Previous research has introduced sensor-integrated systems that provide user feedback to enhance safety and functional mobility. However, a comprehensive overview of their technological and functional characteristics is lacking. To address this gap, this scoping review systematically mapped the current state of research in sensor-based walking aids, focusing on device types, sensor technologies, application contexts, target populations, and reported outcomes. In addition, integrated artificial intelligence (AI)-based approaches for functional support and health monitoring were examined. Following PRISMA-ScR guidelines, 35 peer-reviewed articles were identified from three databases: ACM Digital Library, IEEE Xplore, and Web of Science. Extracted data were thematically analyzed and synthesized across device types (e.g., walking canes, crutches, walkers, rollators) and use cases, including gait training, fall prevention, and daily support. Findings show that, while many prototypes show promising features, few have been evaluated in clinical settings or over extended periods. A lack of standardized methods for sensor location assessment, often the superficial implementation of feedback modalities, and limited integration with other assistive technologies were identified. In addition, system validation and user testing lack consensus, with few long-term studies and often incomplete demographic data. Diversity in data communication approaches and the heterogeneous use of AI algorithms were also notable. The review highlights key challenges and research opportunities to guide the future development of intelligent, user-centered mobility systems.

Smart assistive technologies such as sensor-based footwear and walking aids offer promising opportunities for gait rehabilitation through real-time feedback and patient-centered monitoring. While biofeedback applications show great potential, current research rarely explores integrated closed-loop systems with device- and modality-specific feedback. In this work, we present a modular sensor-based system combining a smart foot orthosis and an instrumented forearm crutch to deliver real-time vibrotactile biofeedback. The system integrates plantar pressure and motion sensing, vibrotactile feedback, and wireless communication via a smartphone application. We conducted a user study with eight participants to validate the system’s feasibility for mobile gait detection and app usability, and to evaluate different vibrotactile feedback types across the orthosis and forearm crutch. The results indicate that pattern-based vibrotactile feedback was rated as more useful and suitable for regular use than simple vibration alerts. Moreover, participants reported clear perceptual differences between feedback delivered via the orthosis and the forearm crutch, indicating device-dependent feedback perception. The findings highlight the relevance of feedback strategy design beyond hardware implementation and inform the development of user-centered haptic biofeedback systems.

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.

The augmentation of smart insoles gained increased interest due to the possibilities in foot-related rehabilitation, medical treatment, and postural risk prevention. As shown by previous work, the technology offers a personalized solution for individual foot morphology and biomechanics, which can monitor the health status and healing process. However, the success of this mobile technology depends on its usability and acceptance by healthcare professionals and patients. Therefore, an exploratory qualitative user study was conducted to investigate the preferences of healthcare professionals and patients regarding augmented smart insoles (ASI) usage. In focus groups, experts and patients explored their needs and suggested potential design solutions. The thematic analysis of the participants’ feedback revealed useful information regarding user interface (UI) designs for the development of a mobile application supporting smart insoles. We found that patients and experts share similar data preferences for a mobile application supporting ASI and that both user groups desired the presence of complete pressure point monitoring, active feedback for posture correction, and personalized training videos for postural risk prevention. However, healthcare professionals expressed concerns regarding the lack of scientific evidence validating the effectiveness of this technology and suggested further research.