基于新颖性检测的跌倒风险预测  

Falls Risk Prediction Based on Novelty Detection

作　　者：刘雅秦 叶宁 徐康[1,2] 王汝传 唐震 LIU Ya-Qin;YE Ning;XU Kang;WANG Ru-Chuan;TANG Zhen(School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks,Nanjing 210023,China;Institute of Advanced Materials,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)

机构地区：[1]南京邮电大学计算机学院、软件学院、网络空间安全学院,南京210023 [2]江苏省无线传感网高技术研究重点实验室,南京210023 [3]南京邮电大学材料科学与工程学院,南京210023

出　　处：《计算机系统应用》2022年第4期163-170,共8页Computer Systems & Applications

基　　金：江苏省科技厅重点研发计划(社会发展)(BE2020713)。

摘　　要：跌倒是65岁及以上人群因伤害致死的第一位原因.结合受试者个体信息的个性化特征,提出一种基于Kinect三维骨架数据的步态特征提取方法,对老年人的跌倒风险进行评估和预测.将跌倒风险分为高跌倒风险和低跌倒风险两类,考虑数据采集的成本问题,采用新颖性检测模型在不平衡数据集下对特征数据进行训练和评估.实验结果表明,OC-SVM(one-class SVM)检测准确率达86.96%,F1-score为88.55%,能够有效地区分低跌倒风险受试者和高跌倒风险受试者.同时,证明了基于Kinect三维骨架数据预测老年人跌倒风险的潜力.Falls are the first cause of injury-related deaths in people over the age of 65.A gait feature extraction method based on Kinect 3D skeleton data is proposed.This method can assess and predict the falls risks of the elderly according to the personalized features of the individual information of the subjects.The falls risks are divided into two classes:high falls risks and low falls risks.Considering the cost of data collection,the novelty detection model is used to train and access the feature data on an unbalanced data set.The experimental results show that the accuracy of one-class support vector machine(OC-SVM)detection is 86.96%and the F1-score is 88.55%,which means the proposed method can effectively distinguish subjects with low falls risks from those with high falls risks.These results also demonstrate the potential of predicting the falls risks of the elderly with Kinect 3D skeleton data.

关 键 词：跌倒风险 新颖性检测 KINECT 步态分析 单类支持向量机 

分 类 号：R319[医药卫生—基础医学] TP391.41[自动化与计算机技术—计算机应用技术]