He Bingze, Shi Ping, Li Xinwei, Fan Meng, Deng Zhipeng, Yu Hongliu
Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2022 Feb 25;39(1):175-184. doi: 10.7507/1001-5515.202103035.
The body weight support rehabilitation training system has now become an important treatment method for the rehabilitation of lower limb motor dysfunction. In this paper, a pelvic brace body weight support rehabilitation system is proposed, which follows the center of mass height (CoMH) of the human body. It aims to address the problems that the existing pelvic brace body weight support rehabilitation system with constant impedance provides a fixed motion trajectory for the pelvic mechanism during the rehabilitation training and that the patients have low participation in rehabilitation training. The system collectes human lower limb motion information through inertial measurement unit and predicts CoMH through artificial neural network to realize the tracking control of pelvic brace height. The proposed CoMH model was tested through rehabilitation training of hemiplegic patients. The results showed that the range of motion of the hip and knee joints on the affected side of the patient was improved by 25.0% and 31.4%, respectively, and the ratio of swing phase to support phase on the affected side was closer to that of the gait phase on the healthy side, as opposed to the traditional body weight support rehabilitation training model with fixed motion trajectory of pelvic brace. The motion trajectory of the pelvic brace in CoMH mode depends on the current state of the trainer so as to realize the walking training guided by active movement on the healthy side of hemiplegia patients. The strategy of dynamically adjustment of body weight support is more helpful to improve the efficiency of walking rehabilitation training.
体重支持康复训练系统现已成为下肢运动功能障碍康复的重要治疗方法。本文提出了一种骨盆支撑体重支持康复系统,该系统跟踪人体的质心高度(CoMH)。旨在解决现有恒阻抗骨盆支撑体重支持康复系统在康复训练过程中为骨盆机构提供固定运动轨迹以及患者康复训练参与度低的问题。该系统通过惯性测量单元收集人体下肢运动信息,并通过人工神经网络预测质心高度,以实现骨盆支撑高度的跟踪控制。所提出的质心高度模型通过偏瘫患者的康复训练进行了测试。结果表明,与传统的具有固定骨盆支撑运动轨迹的体重支持康复训练模型相比,患者患侧髋关节和膝关节的活动范围分别提高了25.0%和31.4%,患侧摆动相与支撑相的比例更接近健康侧的步态阶段。质心高度模式下骨盆支撑的运动轨迹取决于训练者的当前状态,从而实现偏瘫患者健康侧主动运动引导的步行训练。动态调整体重支持的策略更有助于提高步行康复训练的效率。