A new illness recognition framework using frequent temporal pattern mining

Z. Hajihashemi, M. Popescu
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引用次数: 5

Abstract

Living alone in their own residence, older adults are at-risk for late assessment of physical or cognitive changes due to many factors such as their impression that such changes are simply a normal part of aging or their reluctance to admit to a problem. Sensors networks have emerged in the last decade as a possible solution to older adult health monitoring and early illness recognition. Typical early illness recognition approaches are either concentrated on the detection of a given set of activities such as a fall or walks, or on the detection of anomalies such as too many bathroom visits. In this paper we propose a new illness recognition framework, MFA, based on detecting a missing frequent activity from the daily routine. MFA is implemented using a frequent temporal pattern detection algorithm and demonstrated on a pilot dataset collected in TigerPlace, an aging in place community from Columbia, Missouri.
基于频繁时间模式挖掘的疾病识别框架
独居的老年人,由于许多因素,如他们认为这些变化只是衰老的正常部分,或者他们不愿意承认自己有问题,他们在身体或认知方面的变化评估较晚的风险较大。传感器网络在过去十年中出现,作为老年人健康监测和早期疾病识别的可能解决方案。典型的早期疾病识别方法要么集中于检测一组给定的活动,如跌倒或行走,要么集中于检测异常情况,如频繁上厕所。在本文中,我们提出了一种新的疾病识别框架,MFA,基于检测日常生活中缺失的频繁活动。MFA使用频繁的时间模式检测算法实现,并在TigerPlace(一个来自密苏里州哥伦比亚的老龄化社区)收集的试点数据集上进行了演示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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