Ma Huiguo, Bao Yuqi, Jia Chao, Chen Guoqiang, Lan Jingfu, Shi Mingxi, Li He, Guo Qihan, Guan Lei, Li Shuang, Zhang Peng
School of Information Engineering, Quanzhou Ocean Institute, Shishi City, Quanzhou 362700, China.
School of Arts and Design, Yanshan University, Haigang District, Qinhuangdao 066000, China.
Biomimetics (Basel). 2025 Apr 8;10(4):230. doi: 10.3390/biomimetics10040230.
This study aims to address the clinical needs of hemiplegic and stroke patients with lower limb motor impairments, including gait abnormalities, muscle weakness, and loss of motor coordination during rehabilitation. To achieve this, it proposes an innovative design method for a lower limb rehabilitation training system based on Bayesian networks and parallel mechanisms. A Bayesian network model is constructed based on expert knowledge and structural mechanics analysis, considering key factors such as rehabilitation scenarios, motion trajectory deviations, and rehabilitation goals. By utilizing the motion characteristics of parallel mechanisms, we designed a rehabilitation training device that supports multidimensional gait correction. A three-dimensional digital model is developed, and multi-posture ergonomic simulations are conducted. The study focuses on quantitatively assessing the kinematic characteristics of the hip, knee, and ankle joints while wearing the device, establishing a comprehensive evaluation system that includes range of motion (ROM), dynamic load, and optimization matching of motion trajectories. Kinematic analysis verifies that the structural design of the device is reasonable, aiding in improving patients' gait, enhancing strength, and restoring flexibility. The Bayesian network model achieves personalized rehabilitation goal optimization through dynamic probability updates. The design of parallel mechanisms significantly expands the range of joint motion, such as enhancing hip sagittal plane mobility and reducing dynamic load, thereby validating the notable optimization effect of parallel mechanisms on gait rehabilitation.
本研究旨在满足偏瘫和中风后下肢运动功能障碍患者的临床需求，这些功能障碍包括康复期间的步态异常、肌肉无力和运动协调性丧失。为实现这一目标，提出了一种基于贝叶斯网络和并联机构的下肢康复训练系统创新设计方法。基于专家知识和结构力学分析构建贝叶斯网络模型，考虑康复场景、运动轨迹偏差和康复目标等关键因素。利用并联机构的运动特性，设计了一种支持多维度步态矫正的康复训练装置。开发了三维数字模型，并进行了多姿态人体工程学模拟。该研究重点定量评估佩戴该装置时髋、膝和踝关节的运动学特征，建立了一个包括运动范围（ROM）、动态负荷和运动轨迹优化匹配的综合评估系统。运动学分析验证了该装置的结构设计合理，有助于改善患者步态、增强力量和恢复灵活性。贝叶斯网络模型通过动态概率更新实现个性化康复目标优化。并联机构的设计显著扩大了关节运动范围，如增强髋部矢状面活动度并降低动态负荷，从而验证了并联机构对步态康复的显著优化效果。
