基于人体模型约束的步态动态识别方法

Dynamic gait recognition method based on human model constraints

针对外骨骼机器人在人机交互、医疗康复中的人体运动步态准确识别问题,提出一种基于人体模型约束的步态动态识别方法。首先,利用AMS仿真软件建立不同运动的仿真模型,根据模型约束划分步态相位,并通过回归映射建立真实数据与仿真数据间的对应关系;然后,将柔性压力传感器采集的足底压力数据以及惯性测量单元采集的足部位移数据融合为足部运动数据,并根据动态变化结合模型约束条件动态分割运动数据,以判断步态相位;最后,搭建卷积神经网络(CNN)识别行走步态相位。实验结果表明,所提方法的行走动作步态平均识别准确率为94.58%,上、下楼梯动作的平均步态识别准确率分别为93.21%和94.64%,与未经动态分割的足底压力数据的步态识别相比,分别提高了11.34、12.19和16.03个百分点。可见,通过经动态分割的足部运动数据进行CNN识别具有较高的准确率,且适用于不同动作的步态识别。

Aiming at the issue of accurate recognition of human motion gait in exoskeleton robot human computer interaction and medical rehabilitation,a dynamic gait recognition method based on human model constraints was proposed.Firstly,Anybody Modeling System(AMS)simulation software was used to establish different motion simulation models,the gait phases were devided according to the model constraints,and the corresponding relationship between the real data and the simulation data was established through regression mapping.Then,the plantar pressure data collected by the flexible pressure sensor and the foot displacement data collected by the inertial measurement unit were fused into the foot motion data,and the motion data was dynamically segmented according to its dynamic changes and the model constraints to determine the gait phase.Finally,Convolutional Neural Network(CNN)was built to identify the walking gait phase.Experimental results show that the proposed method has the average recognition accuracy of walking action gait of 94.58%,and the average gait recognition accuracy for going upstairs and downstairs actions is 93.21%and 94.64%respectively,which has the gait recognition accuracy of the three actions(walking,going upstairs and downstairs)increased by 11.34,12.19 and 16.03 percentage points,respectively.It can be seen that CNN recognition based on dynamically segmented foot motion data has a high accuracy,and is suitable for gait recognition of different actions.