基于BP神经网络的个性化跌倒检测研究  

Research on Personalized Fall Detection Based on BP Neural Network

作　　者：李楠[1,2] 刘豪 闵亮 LI Nan;LIU Hao;MIN Liang(School of Electrical and Information Engineering,City College,Xi’an Jiaotong University,Xi’an 710018,China;Robot and Intelligent Manufacturing Shaanxi Provincial University Engineering Research Center,Xi’an 710018,China)

机构地区：[1]西安交通大学城市学院,电气信息学院,陕西西安710018 [2]机器人与智能制造陕西省高校工程研究中心,陕西西安710018

出　　处：《微型电脑应用》2024年第6期35-37,41,共4页Microcomputer Applications

基　　金：陕西省科技厅重点研发计划项目(2022GY-089);陕西省高等教育学会2021年高等教育科学研究项目(XGH21295);陕西省教育科学“十四五”规划2021年度课题(SGH21Y0424)。

摘　　要：为了提高跌倒检测的准确性,提出一种基于加速度和克托莱指数的跌倒检测算法。在可穿戴设备获取人体运动数据的同时,根据用户的身高与体重计算克托莱指数,构建共有1080条数据的特征数据集。通过BP神经网络对数据集进行分类,并对跌倒行为进行识别。测试结果表明,算法的识别准确率为98.8%、敏感度为97.9%、特异性为99.4%、检测时间为0.27 s。相较于仅以加速度特征值作为检测数据的跌倒检测算法,所提算法的识别准确率提高了4.9个百分点,敏感度提高了2.9个百分点,特异性提高了6.5个百分点。由此说明算法具备较高的检测精度和实时性,适用于低成本、高性能的可穿戴设备在老年人群体中的普及推广。In order to improve the accuracy of fall detection,a personalized fall detection algorithm based on wearable devices and BP neural network is proposed.When the wearable device obtains the human movement data,it calculates the Ketole index according to the user's height and weight,constructs a feature dataset with a total of 1080 data pieces,classifies the dataset through BP neural network,and identifies the fall behavior.The test results show that the proposed algorithm has a recognition accuracy of 98.8%,a sensitivity of 97.9%,a specificity of 99.4%and a detection time of 0.27 s.Compared with the fall detection algorithm that only takes the acceleration characteristic value as the detection data,the recognition accuracy of the proposed algorithm is increased by 4.9 percentage points,the sensitivity is increased by 2.9 percentage points,and the specificity is increased by 6.5 percentage points.This shows that the algorithm has high detection accuracy and real-time performance,which is suitable for the popularization of low-cost and high-performance wearable devices in the elderly group.

关 键 词：跌倒检测 可穿戴设备 BP神经网络 加速度传感器 克托莱指数 

分 类 号：TP368[自动化与计算机技术—计算机系统结构]