He Chunhua, Liu Shuibin, Zhong Guangxiong, Wu Heng, Cheng Lianglun, Lin Juze, Huang Qinwen
School of Computer, Guangdong University of Technology, Guangzhou 510006, China.
Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Institute of Gerontology, Guangzhou 510080, China.
Micromachines (Basel). 2023 Jan 3;14(1):130. doi: 10.3390/mi14010130.
The ratio of the elderly to the total population around the world is larger than 10%, and about 30% of the elderly are injured by falls each year. Accidental falls, especially bathroom falls, account for a large proportion. Therefore, fall events detection of the elderly is of great importance. In this article, a non-contact fall detector based on a Micro-electromechanical Systems Pyroelectric Infrared (MEMS PIR) sensor and a thermopile IR array sensor is designed to detect bathroom falls. Besides, image processing algorithms with a low pass filter and double boundary scans are put forward in detail. Then, the statistical features of the area, center, duration and temperature are extracted. Finally, a 3-layer BP neural network is adopted to identify the fall events. Taking into account the key factors of ambient temperature, objective, illumination, fall speed, fall state, fall area and fall scene, 640 tests were performed in total, and 5-fold cross validation is adopted. Experimental results demonstrate that the averages of the precision, recall, detection accuracy and are measured to be 94.45%, 90.94%, 92.81% and 92.66%, respectively, which indicates that the novel detection method is feasible. Thereby, this IOT detector can be extensively used for household bathroom fall detection and is low-cost and privacy-security guaranteed.
全球老年人与总人口的比例超过10%，且每年约有30%的老年人因跌倒受伤。意外跌倒，尤其是浴室跌倒，占比很大。因此，老年人跌倒事件检测至关重要。本文设计了一种基于微机电系统热释电红外（MEMS PIR）传感器和热电堆红外阵列传感器的非接触式跌倒探测器，用于检测浴室跌倒。此外，还详细提出了具有低通滤波器和双边界扫描的图像处理算法。然后，提取面积、中心、持续时间和温度的统计特征。最后，采用三层BP神经网络识别跌倒事件。考虑到环境温度、目标、光照、跌倒速度、跌倒状态、跌倒面积和跌倒场景等关键因素，总共进行了640次测试，并采用了5折交叉验证。实验结果表明，精确率、召回率、检测准确率和F1值的平均值分别为94.45%、90.94%、92.81%和92.66%，这表明该新型检测方法是可行的。由此，这种物联网探测器可广泛用于家庭浴室跌倒检测，且成本低、隐私安全有保障。
