Wearable FSR-Based Insole for Real-Time Detection of Gait Events in Rehabilitation

Stroke survivors and individuals with cerebral palsy (CP) often encounter gait irregularities due to their impaired ability to control ankle mobility. Strengthening ankle plantar flexor muscles is crucial for maintaining forward and vertical movement of the body's center of mass, allowing efficient energy transfer during walking. While accelerometers are widely utilized, they necessitate signal processing in order to extract valuable data. In contrast, Force Sensing Resistors (FSR), attached directly to the foot, offer a more direct and reliable method for detecting gait events by measuring foot-ground contact forces. Therefore, this study aims to develop an insole-based monitoring system that provides feedback on ankle plantar flexion during gait monitoring, based on heel strike (HS) and toe off (TO) timing. Unlike commercial FSR insoles focused on weight tracking and pressure distribution, this system will target gait pattern analysis for individuals could not control the ankle plantar flexion. Data collected over a 6 -second walking trial demonstrated consistent and accurate detection of HS and TO events, with clear transitions between gait phases. The binary FSR signals were successfully translated into continuous gait phase data, with phase durations aligning with normal walking condition approximately 60 % in stance and 40 % in swing. Meanwhile, EMG analysis showed increased TA muscle activity during swing, supporting its role in dorsiflexion, while mGas activated in late stance, reflecting its function in plantarflexion for push-off. These findings confirm physiological accuracy and demonstrate coordination between antagonist muscles during walking. The integrated FSR-EMG approach offers a reliable framework for real-time gait monitoring, with potential for clinical applications such as foot drop detection and personalized gait retraining. Future work includes threshold optimization, machine learning classification, real-time feedback, and clinical validation.