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基于雷达的老年人跌倒检测使用光滑的伪Wigner Ville分布和XGBoost学习
Swarubini Pj1, Nagarajan Ganapathy1
1Department of Biomedical Engineering, Indian Institute of Technology, Hyderabad, Kandi, Telangana, India.
Studies in health technology and informatics
|August 23, 2024
概括
基于雷达的跌倒检测使用光滑伪维格纳-维尔分布 (SPWVD) 图像和XGBoost学习准确地识别老年人跌倒. 这种不引人注目的方法为传统的落检测系统提供了一个有希望的替代方案.
科学领域:
- 老年学是一门学科.
- 生物医学工程 生物医学工程
- 信号处理 信号处理
背景情况:
- 老年人的跌倒是一个主要的健康问题,导致严重的发病率和降低生活质量.
- 传统的摔倒检测系统,如可穿戴设备和摄像头,面临限制,包括隐私问题和环境依赖.
- 基于雷达的系统提供了一种不引人注目的,维护隐私的降落检测方法.
研究的目的:
- 为老年人开发和评估一种基于雷达的新型摔倒检测系统.
- 使用光滑伪维格纳-维尔分布 (SPWVD) 图像和XGBoost机器学习来分类秋季事件.
- 评估拟议方法在区分跌倒与非跌倒事件中的准确性和效率.
主要方法:
- 利用在线公开可用的雷达数据库 (N=15) 包含雷达信号.
- 在时间频率表示图像的雷达信号上应用了光滑化伪维格纳-维尔分布 (SPWVD).
- 从SPWVD图像中提取了十个特征,并将它们应用于XGBoost学习,使用十倍交叉验证进行评估.
主要成果:
- 拟议的基于雷达的方法实现了高性能指标,包括最高平均分类准确率为87.47%.
- 关键绩效指标证明了该系统的有效性:f1得分 (87.38%),精度 (88.12%),灵敏度 (86.81%),特异性 (88.31%) 和kappa得分 (74.94%).
- 传统特征与度测量和中位数频率的结合,产生了第二好的性能.
结论:
- 开发的框架证明了在老年人群中精确有效地检测掉落.
- 使用SPWVD图像和XGBoost的基于雷达的摔倒检测是现有方法的可行和有前途的替代方案.
- 该系统的不引人注目的性质使其适合在私人生活空间中部署,增强老年人的安全和独立性.
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