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一种使用热信号的统计分布,由一个独立的低分辨率红外线阵列传感器设备获得的落检测方法

Nishat Tasnim Newaz¹, Eisuke Hanada²

¹Graduate School of Science and Engineering, Saga University, Saga 840-8502, Japan.

Sensors (Basel, Switzerland)

|January 25, 2025

概括

红外线阵列传感器可以通过分析热模式来检测落和识别活动. 这种非侵入性系统有效地将正常运动与跌倒区分开来,增强了安全和医疗监测.

科学领域:

生物医学工程生物医学工程
传感器技术传感器技术
人工智能的人工智能

背景情况:

基于摄像头的系统在医疗监测方面引发了隐私问题.
红外 (IR) 阵列传感器为活动识别和落检测提供了一个非侵入性的替代方案.
保护隐私的技术对于家庭健康监测解决方案至关重要.

研究的目的:

开发和验证一种新的方法,用IR阵列传感器数据来区分正常运动和跌倒.
评估一个具有成本效益的,独立的设备的可行性,用于实时降落检测.
评估统计分布分析用于热模式分类的有效性.

主要方法:

收集了两名从事日常生活活动 (包括落) 的受试者的热量数据.
利用地球移动器距离 (EMD) 将热数据模式与统计分布 (贝塔和正常) 进行比较.
实现了一个基于Raspberry Pi的独立系统,用于实时数据处理.

主要成果:

正常活动显示Beta和正常分布的EMD值较低,表明一致的热模式.
秋季事件显示出高EMD值,这意味着不同的和可变的热信号.
树派系统提供了一个具有成本效益的,独立的解决方案.

结论:

红外线阵列传感器通过热模式分析有效地区分正常运动和跌倒.
拟议的方法提供了一种保护隐私和可靠的方法,用于降落检测和活动识别.
这项技术具有显著的潜力,可以改善医疗监测和降落风险的个人的安全.

关键词:

电子健康管理电子健康管理落检测系统落检测系统落检测系统对于老年人来说,跌倒检测系统.低分辨率的红外线阵列阵列机器学习是机器学习.

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