A Gait Sub-Phase Switching-Based Active Training Control Strategy and Its Application in a Novel Rehabilitation Robot
Junyu Wu, Ran Wang, Zhuoqi Man, Yubin Liu, Jie Zhao, Hegao Cai
TL;DR
This paper introduces a new rehabilitation robot that uses advanced gait recognition to provide personalized, adaptive training for patients recovering from balance disorders.
Contribution
A novel active training control strategy using gait sub-phase recognition to enable personalized rehabilitation robot assistance.
Findings
A hybrid deep neural network model achieved over 99% accuracy in gait sub-phase recognition.
The active training strategy improves patient engagement and autonomous movement during rehabilitation.
The system enables more precise and personalized rehabilitation programs compared to traditional robots.
Abstract
This research study proposes a heuristic hybrid deep neural network (DNN) gait sub-phase recognition model based on multi-source heterogeneous motion data fusion which quantifies gait phases and is applied in balance disorder rehabilitation control, achieving a recognition accuracy exceeding 99%. Building upon this model, a motion control strategy for a novel rehabilitation training robot is designed and developed. For patients with some degree of independent movement, an active training strategy is introduced; it combines gait recognition with a variable admittance control strategy. This strategy provides assistance during the stance phase and moderate support during the swing phase, effectively enhancing the patient’s autonomous movement capabilities and increasing engagement in the rehabilitation process. The gait phase recognition system not only provides rehabilitation…
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Taxonomy
TopicsStroke Rehabilitation and Recovery · Balance, Gait, and Falls Prevention · Prosthetics and Rehabilitation Robotics