Real-Time LSTM-Driven Dynamic Gait Mode Detection for Enhanced Control of Actuated Ankle-Foot Orthosis

Abstract

The implementation of real-time gait mode detection is paramount for providing tailored support to individuals utilizing actuated ankle-foot orthoses (AAFOs), enhancing their walking and mobility. However, existing systems often rely on multiple sensors and struggle with accurate and prompt detection of gait transitions, especially in varied environments. This study develops a novel real-time gait mode detection system that accurately identifies five daily living gait modes including level walking, ramp ascent and descent, and stair ascent and descent using only two foot-mounted inertial measurement units. A long short-term memory based algorithm, trained on data from ten healthy subjects, extracts six kinematic features to predict gait modes. The proposed method integrates this detection system with a taskoriented control strategy to adapt AAFO control according to identified gait modes. Real-time experiments with three healthy participants demonstrated robust gait mode detection, achieving an average accuracy of $98 \pm 1$% across the five modes, even under assistive torque. In trials mimicking abnormal gait, the system maintained an accuracy of $93 \pm 3$%. Additionally, transition delays were analyzed, showing detection can occur between transitions of the leading and trailing foot. The control strategy reduced dorsiflexor and plantar-flexor muscle activation, measured by electromyography, and improved swing phase tracking performance. Detection robustness was further evaluated by walking with obstacles and changes in environmental dimensions.