Exoskeleton driving method based on real moment constraint and continuous gait phase modeling

By using a multi-sensor synchronous acquisition system and continuous gait phase modeling, combined with inverse dynamics calculation and continuous torque mapping, the problems of unstable gait recognition and discontinuous torque output of exoskeletons in complex environments are solved, achieving high-precision torque estimation and smooth control, and improving the naturalness and safety of human-machine collaboration.

CN121267902APending Publication Date: 2026-01-06BEIJING INST OF TECH
0 Cites 3 Cited by

Patent Information

Application Number
CN202511391595.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-26
Publication Date
2026-01-06

AI Technical Summary

Technical Problem

Existing exoskeleton control methods suffer from unstable gait phase recognition, unsmooth mode switching, and a lack of continuity and individualized adaptability in torque output under complex environments, making it difficult to achieve stable and natural human-machine collaborative control.

Method used

A multi-sensor synchronous acquisition system is used to acquire multimodal signal data. Through continuous gait phase modeling and inverse dynamics calculation, the instantaneous torque of the hip and knee joints is predicted by combining multimodal signals. A continuous torque mapping mechanism is designed to convert the torque into drive signals for the exoskeleton actuator.

Benefits of technology

It improves the accuracy and robustness of gait recognition, achieves high-precision estimation of joint torque and smoothness of exoskeleton control, enhances the adaptability and scalability of the system, and improves the human-computer interaction experience.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses an exoskeleton driving method based on real moment constraint and continuous gait phase modeling, which comprises the following steps: acquiring multi-modal signal data in the gait process of a subject by using a multi-sensor synchronous acquisition system, including IMU signals, EMG signals, plantar pressure signals and optical motion capture data; preprocessing the multi-mode signal data to obtain a preprocessed signal; performing continuous gait phase prediction and gait pattern recognition according to the preprocessed signal; estimating a joint instantaneous moment by using a pre-trained moment estimation network based on the predicted gait phase and gait mode; and converting the obtained instantaneous moment of the joint into a driving signal of an exoskeleton actuator. According to the method, discrete event triggering is replaced by continuous gait phase modeling, so that the smoothness and robustness of control are ensured; meanwhile, a mechanical real constraint and individualized moment estimation mechanism is introduced, stable man-machine cooperative control is achieved, and the adaptability and use experience of the exoskeleton in a complex environment are remarkably improved.
Need to check novelty before this filing date? Find Prior Art