Li Xiaoli, Huang Hongshi, Wang Jie, Yu Yuanyuan, Ao Yingfang
College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China.
Institute of Sports Medicine, Peking University Third Hospital, Beijing 100191, China.
Biomed Res Int. 2016;2016:7891407. doi: 10.1155/2016/7891407. Epub 2016 Dec 6.
The movement information of the human body can be recorded in the plantar pressure data, and the analysis of plantar pressure data can be used to judge whether the human body motion function is normal or not. A two-meter footscan® system was used to collect the plantar pressure data, and the kinetic and dynamic gait characteristics were extracted. According to the different description of gait characteristics, a set of models was established according to various people to present the movement of lower limbs. By the introduction of algorithm in machine learning, the FCM clustering algorithm is used to cluster the sample set and create a set of models, and then the SVM algorithm was used to identify the new samples, so as to complete the normal and abnormal motion function identification. The multimodel presented in this paper was carried out into the analysis of the anterior cruciate ligament deficiency. This method demonstrated being effective and can provide auxiliary analysis for clinical diagnosis.
人体的运动信息可记录在足底压力数据中，通过对足底压力数据的分析可判断人体运动功能是否正常。采用两米长的Footscan®系统采集足底压力数据，并提取动力学和动态步态特征。根据对步态特征的不同描述，针对不同人群建立了一组模型来呈现下肢运动。通过引入机器学习算法，使用FCM聚类算法对样本集进行聚类并创建一组模型，然后使用SVM算法对新样本进行识别，从而完成正常和异常运动功能的识别。本文提出的多模型应用于前交叉韧带损伤的分析。该方法证明是有效的，可为临床诊断提供辅助分析。
