AI-Based Gait Analysis System for Rehabilitation: Usability Evaluation of Human-Technology Interaction

Artificial intelligence (AI)-based gait analysis systems are increasingly applied in rehabilitation settings for objective and quantitative assessment of gait function. However, despite their potential, clinical adoption remains limited due to insufficient consideration of usability, user experience, and integration into actual clinical workflows.

This study aimed to conduct a formative evaluation of a prototype AI-based gait analysis system (MediStep M).

A mixed methods formative usability evaluation was conducted with 5 licensed physical therapists. Qualitative data were collected through focus group interviews, and quantitative usability was measured using the system usability scale (SUS). A scenario-based usability assessment was applied to identify user interface challenges, workflow issues, and potential design improvements.

Participants identified major usability barriers, including limited accessibility of the power button, absence of battery status indicators, burdensome manual calibration, and insufficient clinical detail in the gait analysis reports. They also emphasized the need for wireless operation, improved portability, and integration with hospital electronic medical record systems. The mean SUS score was 57 (grade D), indicating suboptimal usability and the need for iterative design refinements.

Although AI-based gait analysis systems hold promise for enhancing rehabilitation outcomes, key usability challenges must be resolved before clinical implementation. Improvements in hardware portability, automated calibration, data management, and user interface design are essential to ensure safety, efficiency, and clinical applicability. These findings provide evidence-based insights to guide iterative development and promote user-centered innovation in AI-based rehabilitation technologies.