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

The implementation of real-time gait mode detection is paramount in effectively providing tailored support for individuals utilizing actuated ankle-foot orthoses (AAFOs), thereby enhancing their walking capabilities and overall mobility. However, existing systems often rely on multiple sensors and struggle with accurate and prompt detection of gait transitions, especially in varied and challenging environments. This study aims to develop a novel real-time gait mode detection system that accurately identifies five essential daily living gait modes, namely level walking, ramp ascent/descent, and stair ascent/descent using only two foot-mounted inertial measurement units (IMUs). By using a long short-term memory (LSTM)-based algorithm trained on data collected from ten healthy subjects, the system extracts six kinematic features to predict gait modes with high accuracy. The proposed method integrates this detection system with a task-oriented control strategy to adapt the control of the AAFO based on the identified gait modes. The real-time experiments involving three healthy participants demonstrated robust gait mode detection, achieving an average estimation accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$98 \pm 1$</tex-math></inline-formula>% across the five gait modes, even with the application of assistive torque. In cases mimicking abnormal gait, the system maintained an accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$93 \pm 3$</tex-math></inline-formula>%. Additionally, each transition delay between gait modes was analyzed, showing that gait mode detection can occur between the transitions of the leading and trailing foot. The results of the control strategy showed a reduction in muscle activation of the dorsiflexor and plantarflexor muscles as measured by EMG, as well as improved tracking performance during the swing phase. Gait mode detection robustness was further evaluated by including walking with obstacles and changes in environmental dimensions.