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首页> 外文期刊>Journal of ambient intelligence and smart environments >Home-based health monitoring of the elderly through gait recognition
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Home-based health monitoring of the elderly through gait recognition

机译：通过步态识别对老人进行家庭健康监测

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摘要

In Europe, in particular, growing numbers of elderly people need sustainable elderly care, which the young are not able to provide. As an alternative, elderly care can be provided through home-based, automatic, health-monitoring systems. Here we propose data-mining algorithms in a system for the automatic recognition of health problems, activities and falls through the analysis of gait. The gait of the elderly is captured using a motion-capture system and the resulting time series of position coordinates are analyzed with a data-mining approach in order to classify it into five health states: 1) normal, 2) with hemiplegia, 3) with Parkinson's disease, 4) with pain in the back and 5) with pain in the leg, or into five activities/falls: 1) accidental fall, 2) unconscious fall, 3) walking, 4) standing/sitting, 5) lying down/lying. We propose and analyze four data-mining approaches: 1) CML - Classical machine-learning approach with raw sensor data, 2) SCML - Classical machine-learning approach with semantic attributes, 3) MDTW - Multidimensional dynamic time-warping approach with raw sensor data and 4) SMDTW - Multidimensional dynamic time-warping approach with semantic attributes. According to the results of the experiments, SMDTW achieved the highest classification accuracy of the four proposed approaches, and transforming the raw data into the semantic attributes significantly improved the performance of the approaches. Since the observed health problems are related also to postural instability and danger of falling, their early detection helps to prevent elderly people from falling.
机译：特别是在欧洲，越来越多的老年人需要年轻人无法提供的可持续的老年人护理。作为替代，可以通过基于家庭的自动健康监控系统提供老人护理。在这里，我们提出了一种通过步态分析自动识别健康问题，活动和跌倒的系统中的数据挖掘算法。使用运动捕捉系统捕获老年人的步态，并使用数据挖掘方法分析位置坐标的时间序列，以将其分为五个健康状态：1）正常，2）偏瘫，3）患有帕金森氏病，4）背部疼痛和5）腿部疼痛或分为5个活动/跌倒：1）意外跌倒，2）无意识跌倒，3）行走，4）站立/坐姿，5）躺着躺下/躺下。我们提出并分析了四种数据挖掘方法：1）CML-具有原始传感器数据的经典机器学习方法，2）SCML-具有语义属性的经典机器学习方法，3）MDTW-具有原始传感器的多维动态时间扭曲方法数据和4）SMDTW-具有语义属性的多维动态时间扭曲方法。根据实验结果，SMDTW达到了所提出的四种方法中最高的分类精度，并且将原始数据转换为语义属性可以显着提高方法的性能。由于观察到的健康问题也与姿势不稳和跌倒的危险有关，因此及早发现有助于防止老人跌倒。

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    Bogdan Pogorelc; Matjaz Gams;

  • 作者单位

    Department of Intelligent Systems, Jozef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia,Spica International d.o.o., Pot k sejmiscu 33, 1231 Ljubljana, Slovenia,Jozef Stefan International Postgraduate School, Jamova 39, 1000 Ljubljana, Slovenia;

    Department of Intelligent Systems, Jozef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia,Spica International d.o.o., Pot k sejmiscu 33, 1231 Ljubljana, Slovenia,Jozef Stefan International Postgraduate School, Jamova 39, 1000 Ljubljana, Slovenia;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    health monitoring; data mining; dynamic time warping; gait recognition; ambient assisted living;

    机译：健康监测;数据挖掘;动态时间扭曲;步态识别环境辅助生活;

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