Evaluation of a Real-Time Feedback-System Design Based on EMG from Tibialis Anterior During Gait

Sammanfattning

Electromyography (EMG) is a promising tool for real-time biofeedback in rehabilitation. This study presents the design and evaluation of a custom iOS application, that detects steps using only EMG signals from the tibialis anterior (TA) and provides real-time feedback via visual, auditory, and haptic modalities. The goal was to evaluate how different feedback types influence muscle activation and user experience during treadmill walking, with a particular focus on potential applications for individuals with foot drop, a condition involving TA weakening due to nerve damage that complicates gait. The results show that reliable step detection durring normal gait can be achieved with a single EMG sensor, using a lightweight peak detection algorithm. Subjective feedback from five participants indicated a strong preference for feedback modalities with low cognitive demand and minimal interference with walking. The circular visual graph stood out for its clarity and intuitive design, while short reinforcement cues, such as vibration pulses or audio tones, were appreciated for their clear indication of a successful step. Objective EMG data revealed increased TA activation during feedback trials, with the highest average increase observed for the circular graph. However, individual responses varied, highlighting the need for feedback options in rehabilitation tools. These findings suggest that the optimal feedback depends on individual preferences, task demands, and stage of recovery, emphasizing the value of multimodal and adaptive feedback systems for future applications in gait rehabilitation.