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

Zhiyu Tian, L. Zhang, Guoan Wang, Xuefeng Wang
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引用次数: 2

Abstract

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.
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基于RGB相机的复杂家庭环境跌倒检测算法
摘要目的:意外跌倒是对老年人健康的威胁。本研究旨在开发一种实时人体跌倒检测系统,以检测老年人的跌倒行为,并提供及时的医疗治疗。方法:设计一种基于RGB摄像机的跌倒检测系统,并在发生跌倒时发送报警信息。该跌落检测系统由硬件和软件算法两部分组成。跌落检测算法包括(1)算法初始化阶段获取环境参数;(2)二维姿态检测,识别人体目标和人体关节位置;(3)根据其实际特点进行肢体长度和多帧跌落判断,确认跌落的发生。结果:将跌倒检测算法与硬件系统相结合,在复杂家庭环境下的测试结果表明,系统灵敏度为94.2%,特异性为96%,准确率为94.5%。结论:与单纯基于动作识别的算法相比,该方法具有更强的鲁棒性。仅使用单目摄像头成本低廉,可以实现实时跌倒检测,帮助老年人在跌倒后得到及时有效的护理。
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