摘要
在康复训练中记录患者的关节运动数据意义重大,关节点的运动轨迹可以由轨迹上一系列的特征点来描述。文章针对使用Kinect 2.0获得的下肢康复运动关节点数据中的误差、抖动等一系列问题,提出了一种加入检验判别的初始化机制和采用简单权值更新策略的快速神经网络模型来修正下肢康复运动的关节点数据。实验结果表明,该模型快速有效,且在运动轨迹随时间推移而改变时响应敏捷。
Records of patients’articulation data in rehabilitation training are significant.Trajectory joints can be described by a series of feature points on the track.In view of the errors,jitter and other problems in the joints data of lower limb rehabilitation exercise obtained by Kinect 2.0 sensor,a fast artificial neural network model using discriminating initialization mechanism and simple weights updating policy is presented with the purpose of correcting joints data.Experimental results show that the model is quick and efficient,and is able to respond agilely when the trajectory changes with time.
作者
何淩立
胡保华
王勇
HE Lingli;HU Baohua;WANG Yong(School of Mechanical Engineering,Hefei University of Technology,Hefei 230009,China)
出处
《合肥工业大学学报（自然科学版）》
CAS
北大核心
2018年第2期159-163,共5页
Journal of Hefei University of Technology：Natural Science
基金
科技部创新基金资助项目(11C26213402042)
芜湖市科技计划资助项目(2015cy04)
