The Research Progress on Sensing Technologies in Lower Limb Exoskeleton Robot Motion Intent Perception Systems

Abstract

Lower limb exoskeleton robots have demonstrated great potential in the field of medical rehabilitation, especially in assisting patients with limited mobility to recover lower limb motor functions. The accuracy of the movement intention recognition system is a critical factor influencing the control effectiveness of exoskeletons. Sensor technology plays a crucial role in this system, as it accurately collects information such as gait phase, joint angles, and muscle activity, ensuring that the exoskeleton can adjust its movement strategy in real-time. This study analyzes the application and development of inertial measurement units (IMUs), plantar pressure sensors, and surface electromyography (sEMG) sensors in movement intention recognition systems. However, issues such as data synchronization, signal noise, and individual differences remain, requiring further optimization of sensor configuration and data processing strategies. Future research will focus on the introduction of intelligent compensation algorithms, multi-sensor fusion, and collaborative sensing, and the optimization of sensor performance, thereby enhancing the system's accuracy, stability, real-time capability, and adaptability, promoting the widespread application of exoskeleton robots in rehabilitation training.