Newaz Nishat Tasnim, Hanada Eisuke
Graduate School of Science and Engineering, Saga University, Saga 840-8502, Japan.
Faculty of Science and Engineering, Saga University, Saga 840-8502, Japan.
Sensors (Basel). 2025 Jan 16;25(2):504. doi: 10.3390/s25020504.
Infrared array sensor-based fall detection and activity recognition systems have gained momentum as promising solutions for enhancing healthcare monitoring and safety in various environments. Unlike camera-based systems, which can be privacy-intrusive, IR array sensors offer a non-invasive, reliable approach for fall detection and activity recognition while preserving privacy. This work proposes a novel method to distinguish between normal motion and fall incidents by analyzing thermal patterns captured by infrared array sensors. Data were collected from two subjects who performed a range of activities of daily living, including sitting, standing, walking, and falling. Data for each state were collected over multiple trials and extended periods to ensure robustness and variability in the measurements. The collected thermal data were compared with multiple statistical distributions using Earth Mover's Distance. Experimental results showed that normal activities exhibited low EMD values with Beta and Normal distributions, suggesting that these distributions closely matched the thermal patterns associated with regular movements. Conversely, fall events exhibited high EMD values, indicating greater variability in thermal signatures. The system was implemented using a Raspberry Pi-based stand-alone device that provides a cost-effective solution without the need for additional computational devices. This study demonstrates the effectiveness of using IR array sensors for non-invasive, real-time fall detection and activity recognition, which offer significant potential for improving healthcare monitoring and ensuring the safety of fall-prone individuals.
基于红外阵列传感器的跌倒检测与活动识别系统，作为增强各种环境下医疗监测与安全的有前景的解决方案，已获得发展势头。与可能侵犯隐私的基于摄像头的系统不同，红外阵列传感器提供了一种非侵入性、可靠的跌倒检测与活动识别方法，同时保护了隐私。这项工作提出了一种通过分析红外阵列传感器捕获的热模式来区分正常运动和跌倒事件的新方法。数据来自两名进行了一系列日常生活活动的受试者，包括坐、站、走和跌倒。每个状态的数据在多次试验和较长时间内收集，以确保测量的稳健性和可变性。使用推土机距离将收集到的热数据与多种统计分布进行比较。实验结果表明，正常活动在贝塔分布和正态分布下表现出较低的EMD值，表明这些分布与常规运动相关的热模式密切匹配。相反，跌倒事件表现出较高的EMD值，表明热特征的变异性更大。该系统使用基于树莓派的独立设备实现，提供了一种经济高效的解决方案，无需额外的计算设备。这项研究证明了使用红外阵列传感器进行非侵入性实时跌倒检测和活动识别的有效性，这对于改善医疗监测和确保易跌倒个体的安全具有巨大潜力。
