Real-time Human Motion Intention Recognition for Powered Wearable Hip Exoskeleton using LSTM Networks

Lower limb exoskeletons are revolutionizing rehabilitation and assistive technologies. However, accurate and real-time recognition of user movement intent remains a challenge. This paper proposes a Long Short-Term Memory (LSTM) network-based approach for real-time motion intention recognition in a powered wearable hip exoskeleton. We leverage biomechanical signals from an embedded IMU sensor and the exoskeleton’s joint angle and velocity data to classify various motion patterns, including standing, sitting, walking, stair ascending/descending, and activity transitions like sit-to-stand and stand-to-sit. The paper details the hardware architecture of the exoskeleton, data collection and processing procedures, and the LSTM model architecture. We evaluate the performance of the proposed method through experiments and the results indicate the effectiveness of the model. The results of this study may have significant implications for the development and application of lower limb exoskeletons, particularly in the field of rehabilitation.