Wu Junyu, Wang Ran, Man Zhuoqi, Liu Yubin, Zhao Jie, Cai Hegao
State Key Laboratory of Robot Technology and Systems, Harbin Institute of Technology, Harbin 150001, China.
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China.
Biosensors (Basel). 2025 Jun 4;15(6):356. doi: 10.3390/bios15060356.
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 practitioners with a comprehensive tool for patient assessment but also serves as a theoretical foundation for collaborative control in rehabilitation robots. Through the innovative active-passive training control strategy and its application in the novel rehabilitation robot, this research study overcomes the limitations of traditional rehabilitation robots, which typically operate in a single functional mode, thereby expanding their functional boundaries and enabling more precise, personalized rehabilitation training programs tailored to the needs of patients in different stages of recovery.
本研究提出了一种基于多源异构运动数据融合的启发式混合深度神经网络（DNN）步态子阶段识别模型，该模型可对步态阶段进行量化，并应用于平衡障碍康复控制，识别准确率超过99%。在此模型基础上，设计并开发了一种新型康复训练机器人的运动控制策略。对于具有一定程度自主运动能力的患者，引入了主动训练策略；该策略将步态识别与可变导纳控制策略相结合。此策略在站立阶段提供辅助，在摆动阶段提供适度支持，有效增强了患者的自主运动能力，并提高了其在康复过程中的参与度。步态阶段识别系统不仅为康复从业者提供了全面的患者评估工具，也为康复机器人的协同控制奠定了理论基础。通过创新的主动-被动训练控制策略及其在新型康复机器人中的应用，本研究克服了传统康复机器人通常以单一功能模式运行的局限性，从而拓展了其功能边界，能够为处于不同恢复阶段的患者量身定制更精确、个性化的康复训练方案。
