{"title":"标签-跌倒:使用 RFID 无源标签的基于多普勒频移的跌倒检测方法","authors":"Kai Huang;Yongtao Ma;Yicheng Chu;Zemin Wang","doi":"10.1109/JRFID.2024.3393242","DOIUrl":null,"url":null,"abstract":"As the global population ages, the prevalence of elderly individuals living independently has risen. As one of the main threats to the health of the elderly, falling seriously reduces the happiness of the elderly and imposes a burden on the medical system. Therefore, the exploration of automatic fall detection systems is crucial. However, proposed fall detection systems exhibit varying degrees of shortcomings. In this paper, we propose a new fall detection method utilizing Doppler shift with RFID passive tags. The motion of the passive tag induces a Doppler shift in the reflected signal. This method is the first to use Doppler frequency shift for fall detection in RFID. Additionally, a velocity-position iteration algorithm is applied to ascertain the tag’s position and velocity over time. The combination of velocity and position for fall detection yields higher accuracy compared to individual parameters. The proposed method demonstrates the capability to differentiate between sudden and soft falls, aiding medical professionals in identifying the cause of a user’s fall. The experimental results demonstrate that the system achieves an accuracy rate of 91.7% in detecting sudden falls, and this accuracy remains at 86.8% even after incorporating soft falls into the analysis. Consequently, the proposed method proves to be an effective and reliable approach for fall detection.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Tag-Fall: A Doppler Shift-Based Fall Detection Method Using RFID Passive Tags\",\"authors\":\"Kai Huang;Yongtao Ma;Yicheng Chu;Zemin Wang\",\"doi\":\"10.1109/JRFID.2024.3393242\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As the global population ages, the prevalence of elderly individuals living independently has risen. As one of the main threats to the health of the elderly, falling seriously reduces the happiness of the elderly and imposes a burden on the medical system. Therefore, the exploration of automatic fall detection systems is crucial. However, proposed fall detection systems exhibit varying degrees of shortcomings. In this paper, we propose a new fall detection method utilizing Doppler shift with RFID passive tags. The motion of the passive tag induces a Doppler shift in the reflected signal. This method is the first to use Doppler frequency shift for fall detection in RFID. Additionally, a velocity-position iteration algorithm is applied to ascertain the tag’s position and velocity over time. The combination of velocity and position for fall detection yields higher accuracy compared to individual parameters. The proposed method demonstrates the capability to differentiate between sudden and soft falls, aiding medical professionals in identifying the cause of a user’s fall. The experimental results demonstrate that the system achieves an accuracy rate of 91.7% in detecting sudden falls, and this accuracy remains at 86.8% even after incorporating soft falls into the analysis. Consequently, the proposed method proves to be an effective and reliable approach for fall detection.\",\"PeriodicalId\":73291,\"journal\":{\"name\":\"IEEE journal of radio frequency identification\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE journal of radio frequency identification\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10508185/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE journal of radio frequency identification","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10508185/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
摘要
随着全球人口老龄化的加剧,独立生活的老年人越来越多。作为老年人健康的主要威胁之一,跌倒严重降低了老年人的幸福感,也给医疗系统带来了负担。因此,探索自动跌倒检测系统至关重要。然而,目前提出的跌倒检测系统都存在不同程度的缺陷。在本文中,我们提出了一种利用多普勒频移和 RFID 无源标签的新型跌倒检测方法。无源标签的运动会引起反射信号的多普勒频移。该方法首次将多普勒频移用于 RFID 的跌倒检测。此外,还采用了速度-位置迭代算法来确定标签随时间变化的位置和速度。与单个参数相比,结合速度和位置进行跌倒检测的准确度更高。所提出的方法证明了区分突然跌倒和软跌倒的能力,有助于医疗专业人员识别用户跌倒的原因。实验结果表明,该系统检测突然跌倒的准确率达到 91.7%,即使将软跌倒纳入分析,准确率也保持在 86.8%。因此,所提出的方法被证明是一种有效、可靠的跌倒检测方法。
Tag-Fall: A Doppler Shift-Based Fall Detection Method Using RFID Passive Tags
As the global population ages, the prevalence of elderly individuals living independently has risen. As one of the main threats to the health of the elderly, falling seriously reduces the happiness of the elderly and imposes a burden on the medical system. Therefore, the exploration of automatic fall detection systems is crucial. However, proposed fall detection systems exhibit varying degrees of shortcomings. In this paper, we propose a new fall detection method utilizing Doppler shift with RFID passive tags. The motion of the passive tag induces a Doppler shift in the reflected signal. This method is the first to use Doppler frequency shift for fall detection in RFID. Additionally, a velocity-position iteration algorithm is applied to ascertain the tag’s position and velocity over time. The combination of velocity and position for fall detection yields higher accuracy compared to individual parameters. The proposed method demonstrates the capability to differentiate between sudden and soft falls, aiding medical professionals in identifying the cause of a user’s fall. The experimental results demonstrate that the system achieves an accuracy rate of 91.7% in detecting sudden falls, and this accuracy remains at 86.8% even after incorporating soft falls into the analysis. Consequently, the proposed method proves to be an effective and reliable approach for fall detection.