An RGB camera-based fall detection algorithm in complex home environments

Abstract Objectives: Accidental falls are a threat to the well-being of older people. This study aimed to develop a real-time human fall detection system to detect fall behaviors and provide timely medical treatment for older adults. Methods: An RGB camera-based fall detection system is designed and...

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Vydáno v:Interdisciplinary nursing research Ročník 1; číslo 1; s. 14 - 26
Hlavní autoři: Tian, Zhiyu, Zhang, Liang, Wang, Guoan, Wang, Xuefeng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hagerstown, MD Lippincott Williams & Wilkins 29.11.2022
Témata:
Fall detection
Pose detection
RGB camera
ISSN:2832-918X, 2832-918X
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Shrnutí:Abstract Objectives: Accidental falls are a threat to the well-being of older people. This study aimed to develop a real-time human fall detection system to detect fall behaviors and provide timely medical treatment for older adults. Methods: An RGB camera-based fall detection system is designed and it can send alarm messages when a fall occurs. This fall detection system consists of two design aspects: a hardware and a software algorithm. The fall detection algorithm includes (1) algorithm initialization phase to obtain environmental parameters; (2) 2-dimensional pose detection to identify human targets and human joint locations; and (3) limb-length and multiframe fall judgment to confirm the occurrence of falls based on its practical features. Results: By combining fall detection algorithms with a hardware system, the test results in complex home environments showed that the system sensitivity was 94.2%, the specificity was 96%, and the accuracy was 94.5%. Conclusion: The proposed method is more robust compared with the algorithm based exclusively on action recognition. Using only a monocular camera is cost-friendly and can realize real-time fall detections, and help older people to get timely and effective care after a fall.
Bibliografie:Corresponding author: Xuefeng Wang. Data Availability Statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Published online 29 November 2022
ISSN:2832-918X
2832-918X
DOI:10.1097/NR9.0000000000000007