摘要
为减少老年人跌倒事件,在实验室采集54位老年人行进过程中的运动学数据;着眼于胸椎、膝盖、肩胛骨、骨盆等4个位置,以关节点在矢状面、冠状面、横断面的平均位移作为特征维度构建预测模型,应用支持向量机(SVM)算法对易跌倒老人进行识别预测;并通过数据对比,得到可以实现较高预测精度的最小维度。结果表明:通过粒子群算法(PSO)和基因算法(GA)优化SVM参数后,模型预测精度为87. 5%;通过骨盆位置建立的3个维度可以达到同样的预测精度。
In order to reduce the falls of the elderly,the kinematics data from 54 elderly people were collected in the laboratory.Taking the thoracic spine,knee,scapula and pelvis as the research objects,and the average displacement of joint points in sagittal plane,coronal plane and cross-section as the feature dimension,the prediction model was constructed,and the SVM algorithm was applied to indentify and predict elderly people who are likely to fall.By comparing the data,the minimum dimension that can achieve higher prediction accuracy was obtained.The results show that the prediction accuracy of the proposed model is 87.5%when the parameters of SVM are optimized by particle swarm optimization(PSO)and genetic algorithm(GA),and that the same prediction accuracy can be achieved by establishing three dimensions through pelvic position.
作者
马英楠
吕子阳
高星
王勇毅
MA Yingnan;LYU Ziyang;GAO Xing;WANG Yongyi(School of Biological and Medical Engineering,Beihang University,Beijing 100083,China;School of Safety and Environmental Engineering,Capital University of Economics and Business,Beijing 100070,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2019年第6期43-48,共6页
China Safety Science Journal
基金
北京市科学技术研究院创新团队计划课题基金资助(IG201704C1)
北京城市系统工程研究中心自立课题基金资助(2019C010)
