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Modeling elderly behavioral patterns in single-person households

机译：在单人家庭中建模老年行为模式

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摘要

This paper proposes and describes an unsupervised computational model that monitors an elderly person who lives alone and issues alarms when a risk to the elderly person's well-being is identified. This model is based on data extracted exclusively from passive infrared motion sensors connected to a ZigBee wireless network. The proposed monitoring system and model is non-intrusive, does not capture any images, and does not require any interaction with the monitored person. Thus, it is more likely to be adopted by members of the elderly population who might reject other more intrusive or complex types of technology. The developed computational model for activity discovery employs a kernel estimator and local outlier factor calculation, which are reliable and have a low computational cost. This model was tested with data collected over a period of 25 days from two elderly volunteers who live alone and have fairly different routines. The results demonstrate the model's ability to learn relevant behaviors, as well as identify and issue alarms for atypical activities that can be suggestive of health problems. This low-cost, minimalistic sensor network approach is especially suited to the reality of underdeveloped (and developing) countries where assisted living communities are not available and low cost and ease of use are paramount.
机译：本文提出并描述了一个无监督的计算模型，监测一个人的老年人，当识别老年人幸福的风险时，遇到警报。该模型基于专门从连接到ZigBee无线网络的被动红外运动传感器提取的数据。建议的监测系统和模型是非侵入性的，不会捕获任何图像，并且不需要与监控人员进行任何互动。因此，更有可能被老年人的成员采用，他们可能拒绝其他更具侵入性或复杂的技术。活动发现的开发的计算模型采用内核估计器和本地异常因素系数计算，可靠性并且具有低计算成本。该模型通过从两名老人志愿者收集的数据进行了测试，他们独自生活，并具有相当不同的惯例。结果展示了模型的学习相关行为的能力，以及识别和发布可能具有暗示健康问题的非典型活动的警报。这种低成本，简约的传感器网络方法特别适用于欠发达（和开发）国家的现实，辅助生活社区无法获得，低成本和易用性是至关重要的。

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