重庆大学学报2025，Vol.48Issue(6)：112-122,11.DOI:10.11835/j.issn.1000-582X.2025.06.010
基于SVM算法的跌倒预测及保护系统研究
Research on fall prediction and protection system based on SVM
摘要
Abstract
Real-time fall prediction and protection systems can significantly reduce fall-related injury risks while enhancing independence,physical well-being,and mental health of elderly individuals living alone.To improve fall prediction algorithm performance,specifically recognition accuracy,recall rate,and specificity,while minimizing both fall misclassification errors and airbag deployment time,this study proposes a multi-threshold fall prediction algorithm based on support vector machines(SVM),integrated with an airbag protection system.Motion data are first collected through a waist-worn acceleration sensor.Then,the SVM algorithm determines optimal thresholds for acceleration,velocity,and posture angle to differentiate falls from activities of daily living(ADLs).Finally,the optimized algorithm is deployed on a microcontroller to enable real-time fall prediction and trigger the airbag system.Experimental results show that the system achieves 97.3%accuracy,99%recall and 96.1%specificity in fall recognition,with an average airbag inflation time of 350.4 ms.These metrics confirm both high prediction reliability and rapid protective response,validating the system's effectiveness for real-time fall prediction and protection.关键词
支持向量机/分裂法/跌倒预测/跌倒保护系统Key words
SVM/splitting technique/fall prediction/fall protection system分类
计算机与自动化引用本文复制引用
彭磊,曹治东,晁瑞,李小虎,胡建华,李新超..基于SVM算法的跌倒预测及保护系统研究[J].重庆大学学报,2025,48(6):112-122,11.基金项目
重庆市科卫联合项目(2020MSXM111,2023MSXM023) （2020MSXM111,2023MSXM023）
中央高校基本科研业务费医工融合项(2021CDJYGRH011).Supported by Science and Health Joint Project of Chongqing(2020MSXM111,2023MSXM023),Central Universities Project in China(2021CDJYGRH011). （2021CDJYGRH011）
