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利用可穿戴传感器检测和分类老年人异常行为的人工智能方法。

Artificial intelligence approach for detecting and classifying abnormal behaviour in older adults using wearable sensors.

作者信息

Liu Xiaojun, Chau Ka Yin, Zheng Junxiong, Deng Dongni, Tang Yuk Ming

机构信息

School of Business, Shenzhen City Polytechnic, Shenzhen, China.

Skilled Society Research Center of Shenzhen Institute of Technology, Shenzhen, China.

出版信息

J Rehabil Assist Technol Eng. 2024 Oct 30;11:20556683241288459. doi: 10.1177/20556683241288459. eCollection 2024 Jan-Dec.

DOI:10.1177/20556683241288459
PMID:39493271
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11528604/
Abstract

The global population of older adults has increased, leading to a rising number of older adults in nursing homes without adequate care. This study proposes a smart wearable device for detecting and classifying abnormal behaviour in older adults in nursing homes. The device utilizes artificial intelligence technology to detect abnormal movements through behavioural data collection and target positioning. The intelligent recognition system and hardware sensors were tested using cloud computing and wireless sensor networks (WSNs), comparing their performance with other technologies through simulations. A triple-axis acceleration sensor collected motion behaviour data, and Zigbee enabled the wireless transfer of the sensor data. The Backpropagation (BP) neural network detected and classified abnormal behaviour based on simulated sensor data. The proposed smart wearable device offers indoor positioning, detection, and classification of abnormal behaviour. The embedded intelligent system detects routine motions like walking and abnormal behaviours such as falls. In emergencies, the system alerts healthcare workers for immediate safety measures. This study lays the groundwork for future AI-based technology implementation in nursing homes, advancing care for older adults.

摘要

全球老年人口数量增加，导致养老院中缺乏足够护理的老年人数量不断上升。本研究提出了一种智能可穿戴设备，用于检测和分类养老院中老年人的异常行为。该设备利用人工智能技术，通过行为数据收集和目标定位来检测异常动作。利用云计算和无线传感器网络（WSN）对智能识别系统和硬件传感器进行了测试，并通过模拟将其性能与其他技术进行了比较。三轴加速度传感器收集运动行为数据，Zigbee实现传感器数据的无线传输。反向传播（BP）神经网络基于模拟传感器数据检测和分类异常行为。所提出的智能可穿戴设备可实现室内定位、异常行为检测和分类。嵌入式智能系统可检测诸如行走等常规动作以及跌倒等异常行为。在紧急情况下，系统会提醒医护人员采取即时安全措施。本研究为未来养老院基于人工智能的技术实施奠定了基础，推动了对老年人的护理。

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