人体在行走、奔跑等运动时，在运动神经中枢的支配下，下肢运动存在一定的协调关系。这种协调性是生物进化和后天学习的结果，它使人体运动更加灵活、高效。研究健康人体的下肢运动协调性对于中风病人的康复治疗、足式机器人的步态规划、外骨骼机器人等助力设备的人机协同控制等领域具有重要意义。目前人体下肢协调性的研究仍比较初级，多停留在现象分析和理论表述，缺少准确实用的建模方法。该文利用可穿戴运动捕捉系统对人体下肢在行走和奔跑时的运动协调性进行测量和分析，基于神经网络模型提出大小腿运动协调性的建模方法。实验结果证明了大小腿运动协调模型的存在，基于该模型，可根据小腿的运动信息准确预估大腿和膝关节的运动信息。该研究结果可应用于康复医疗、机器人步态规划、智能助力设备等领域，将积极促进相关领域技术进步。
When a human is walking or running, the coordinated relationship between the movements of the thigh and shank are controlled by the motor nerve system. This relationship between the parts of the lower limb is the result of evolution and experience, which makes human movement more flexible and efficient. Research on the coordination of the lower limbs for healthy people facilitates rehabilitation of stroke patients, robot gait planning, and human-machine collaboration of assistive equipment such as exoskeletons. At present, the research on the coordination of human lower limb motion is still relatively basic, mostly focusing on observations and theoretical analyses, with few practical models. This study used a wearable motion capture system to measure and model the motion relationships of the lower limbs for human walking and running. A neural network model was then developed to characterize the coordination of the thigh and shank. This model accurately predicts the motion of the thigh and the knee joint from the shank movement information. This motion model can be used for rehabilitation, robot gait planning, intelligent assisting equipment and other related fields.
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