Development of a Mixed-Control Ankle Assist Device with Sensor-Fusion-Based Phase Recognition for Walking Exercise Promotion

Abstract

"Frail" elderly often experience walking impairments that limit independence and sustained physical activity. Although various assistive devices exist, many rely on single-mode control, limiting adaptability, responsiveness to gait variability, and voluntary motion. To improve, we developed a wearable ankle-assist device with real-time gait phase recognition and multi-mode control. Sensor fusion of inertial and plantar-pressure data enables robust five-phase segmentation, with optimal weights tuned by Particle Swarm Optimization. Based on detected gait phase, the controller dynamically switches between speed, torque, and free modes, adapting to cadence variations. Treadmill experiments showed that mixed control increased walking distance (251 m to 282 m (p < 0.05)), reduced heart rate change (20% to 10% (p < 0.01)). Gait analysis confirmed comfort and less resistance. These findings demonstrate that phase-aware adaptive assistance balances propulsion and natural motion, supporting mobility and reducing strain. This framework provides a practical basis for wearable ankle-assist systems in elderly rehabilitation and daily use.