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<p>Older adults face increased health risks, especially accidental injuries such as falls, which are one of the top ten causes of death in this age group. To address these changes, recent innovations have focused on developing advanced monitoring technologies to detect and prevent accidents in real time. Among these, fall detection systems have emerged as a critical area of research. This study aimed to evaluate the ability of millimeter-wave (mmWave) sensors to accurately detect multiple falls in the presence of large obstacles in a large, real-world indoor space. The mmWave sensors employed the Doppler effect to capture a human body’s point cloud and track its center point to estimate body position and identify fall events. A 12 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation> 12 meter indoor test area was established for the trials. The mmWave system’s accuracy was validated with video ground truth. Multiple sensors and azimuth tests were conducted to optimize radar configurations. 10 participants performed multiple human fall detection trials under 10 different scenarios. We have successfully validated the mmWave system with the video ground truth. In fall detection testing, the mmWave system achieved an overall accuracy of 97.9% across 10 multi-person scenarios. The results show that the system’s fall detection false negative rate increases with the number of subjects. This study validated the performance of a mmWave system for fall detection in a large indoor environment, demonstrating a high accuracy of 97.9% in a multi-person scenario. However, performance varied with crowd density, showing a correlation between increased false negative rates and the number of subjects due to occlusion. This study supports that mmWave technology offers good capabilities for fall accident monitoring in large indoor spaces with both privacy protection and convenience.</p><p><i>IRB Registry</i>: Institutional Review Board of the Chang Gung Medical Foundation, Approval No.: 202500191B0, Registration date: 3 March 2025.</p>

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Millimeter-wave technology for multi-person fall detection validated through wearable sensors and real-life scenarios

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月25日

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  • Hsiang-Ho Chen,
  • Jui-Da Lin,
  • Shu-Hsuan Lin,
  • Chieh-Wei Wu,
  • Hsin-Chang Chen

摘要

Older adults face increased health risks, especially accidental injuries such as falls, which are one of the top ten causes of death in this age group. To address these changes, recent innovations have focused on developing advanced monitoring technologies to detect and prevent accidents in real time. Among these, fall detection systems have emerged as a critical area of research. This study aimed to evaluate the ability of millimeter-wave (mmWave) sensors to accurately detect multiple falls in the presence of large obstacles in a large, real-world indoor space. The mmWave sensors employed the Doppler effect to capture a human body’s point cloud and track its center point to estimate body position and identify fall events. A 12 \(\times\) 12 meter indoor test area was established for the trials. The mmWave system’s accuracy was validated with video ground truth. Multiple sensors and azimuth tests were conducted to optimize radar configurations. 10 participants performed multiple human fall detection trials under 10 different scenarios. We have successfully validated the mmWave system with the video ground truth. In fall detection testing, the mmWave system achieved an overall accuracy of 97.9% across 10 multi-person scenarios. The results show that the system’s fall detection false negative rate increases with the number of subjects. This study validated the performance of a mmWave system for fall detection in a large indoor environment, demonstrating a high accuracy of 97.9% in a multi-person scenario. However, performance varied with crowd density, showing a correlation between increased false negative rates and the number of subjects due to occlusion. This study supports that mmWave technology offers good capabilities for fall accident monitoring in large indoor spaces with both privacy protection and convenience.

IRB Registry: Institutional Review Board of the Chang Gung Medical Foundation, Approval No.: 202500191B0, Registration date: 3 March 2025.