Gait prediction for lower limb exoskeleton robots based on real-time adaptive Kalman filtering

This paper presents a gait prediction method for lower limb exoskeleton robots using a real-time adaptive Kalman filtering algorithm. The exoskeleton robot targets two user groups: individuals with impaired lower limb motor function requiring rehabilitation training, where the device aids in muscle exercise during walking to facilitate recovery, and healthy individuals using it as a wearable assistive device. To enhance movement intention prediction and improve human-machine coordination, this study focuses on the gait prediction algorithm for walking assistance in healthy users and proposes a gait prediction control strategy based on normal gait orientation. The control system utilizes a microcontroller and Raspberry Pi as its core, enabling functional mode selection through multi-sensor data fusion and effective control of the robot via Bluetooth communication. By comparing the original model algorithm with the proposed real-time updating Kalman filter algorithm, the latter demonstrates feasibility, achieving a prediction error within 1°. This validates the model’s effectiveness in real-time gait prediction.