摘要
目的 通过深度相机和神经网络估计人在直线行走时髋、膝和踝关节的屈伸力矩.方法 利用光学运动捕捉系统、测力板和Azure Kinect深度相机采集20个人的步态信息,受试者被要求以其偏好的步行速度直线行走,同时踏在测力板上.并利用Visual 3D仿真得到关节力矩作为参考值,分别训练人工神经网络(artificial neural network,ANN)模型与长短期记忆(long short-term memory,LSTM)模型进行关节力矩估计.结果 ANN模型估计髋、膝和踝关节的关节力矩的相对均方根误差(relative root mean square error,rRMSE)分别为15.87％～17.32％、18.36％～25.34％和 14.11％～16.82％,相关系数分别为 0.81～0.85、0.69～0.74 和 0.76～0.82.LSTM 模型具有更好的估计效果,rRMSE分别为8.53％～12.18％、14.32％～18.78％和6.51％～11.83％,相关系数分别达到了 0.89～0.95、0.85～0.91和0.90～0.97.结论 本文证实了利用深度相机和神经网络无接触估计人体下肢关节力矩方案的可行性,其中LSTM模型具有更佳的表现.关节力矩估计结果与现有研究相比具有更好的精度,潜在应用场景包含远程医疗、个性化康复方案制定以及矫形器辅助设计等.
Abstract
Objective To estimate the flexion and extension torques of the hip,knee,and ankle joints during straight-line walking using depth cameras and neural networks.Methods Gait information was collected from 20 individuals using an optical motion capture system,force plates,and an Azure Kinect depth camera.The subjects were asked to walk straight at their preferred speed while stepping on the force plates.The joint torques were obtained using visual 3D simulation as a reference value,and an artificial neural network(ANN)and long short-term memory(LSTM)network were trained to estimate the joint torques.Results The relative root mean square errors(rRMSEs)of the ANN model for estimating the joint torques of hip,knee,and ankle were 15.87％-17.32％,18.36％-25.34％,and 14.11％-16.82％,respectively,and the correlation coefficients were 0.81-0.85,0.69-0.74 and 0.76-0.82,respectively.The LSTM model had a better estimation effect,with rRMSEs of 8.53％-12.18％,14.32％-18.78％,and 6.51％-11.83％,and correlation coefficients of 0.89-0.95,0.85-0.91 and 0.90-0.97,respectively.Conclusions This study confirms the feasibility of using a depth camera and neural network for noncontact estimation of lower limb joint torques,and LSTM has a better performance.Compared with existing studies,the joint torque estimation results have better accuracy,and the potential application scenarios include telemedicine,personalized rehabilitation program development,and orthosis-assisted design.
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