Method and system for controlling lower limb exoskeleton under brain-computer interface intention confidence

By acquiring intention confidence and gait phase information through brain-computer interface and performing neurally driven variable impedance parameter mapping, the problem of insufficient adaptive adjustment and safety protection of existing lower limb exoskeleton systems in patients with neurological damage is solved, realizing natural adaptive and highly safe rehabilitation training.

CN121979398BActive Publication Date: 2026-06-30HANGZHOU ROBOCT TECH DEV CO LTD
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Patent Information

Application Number
CN202610450815.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-04-08
Publication Date
2026-06-30
Estimated Expiration
2046-04-08

AI Technical Summary

Technical Problem

Existing lower limb exoskeleton rehabilitation systems cannot fully utilize the continuous confidence of motor intention for dynamic control in the rehabilitation training of patients with neurological damage. This results in the system being unable to adaptively adjust joint stiffness and damping, ignoring neural matching errors, and having insufficient safety protection mechanisms.

Method used

By acquiring the confidence level of intent through brain-computer interface technology, combining gait phase information to perform neurally driven variable impedance parameter mapping, obtaining motion position error and interaction force error, performing closed-loop fusion processing, evaluating human-machine coupling compliance and performing safety gain correction, and achieving compliant closed-loop control.

Benefits of technology

It achieves natural adaptation and high safety protection in human-machine collaborative rehabilitation training, eliminates the dragging feeling of traditional systems, improves the smoothness of trajectory tracking under complex gait, and avoids secondary mechanical injuries.

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Abstract

This application discloses a method and system for controlling a lower limb exoskeleton using a brain-computer interface based on intent confidence, relating to the field of medical rehabilitation technology. First, the system identifies the original electroencephalogram (EEG) signal to obtain intent confidence, and maps an impedance parameter set including joint stiffness and damping coefficients by combining gait phase information. Second, it extracts motion position and interaction force errors, compares the intent confidence with the actual execution state using a sliding time window to obtain neural matching errors, and merges these three into a total composite error. Simultaneously, it assesses human-machine coupling compliance based on changes in human joint angles and exoskeleton torque, and generates a safety gain coefficient. Finally, it performs gain calculations on the total composite error based on the impedance parameter set, and corrects it with the safety gain coefficient to obtain the target control torque for output to the actuator. This constructs a neural-force-motor three-loop architecture, achieving compliant and adaptive on-demand assistance and highly safe human-machine collaborative rehabilitation with defensive protection.
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