Smart home-based longitudinal functional assessment

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication Pub Date : 2014-09-13 DOI:10.1145/2638728.2638813

Prafulla N. Dawadi, D. Cook, M. Schmitter-Edgecombe

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引用次数: 25

Abstract

In this paper, we investigate methods of performing automated cognitive health assessment from smart home sensor data. Specifically, we introduce an algorithm to quantify and track changes in activities of daily living and in the mobility of a smart home resident over time using longitudinal smart home sensor data. We use an automated activity recognition algorithm to recognize a smart home resident's activities of daily living from the generated sensor data, and introduce a Compare and Count (2C) algorithm to quantify the changes in everyday behavior. We test our approach using a longitudinal sensor dataset that we collected from 18 single-resident smart homes for nearly two years and study the relationship between observed changes in the sensor-based everyday functioning parameters and changes in standard clinical health assessment scores. The results suggest that we may be able to develop sensor-based change algorithms that can predict specific components of cognitive and physical health.

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基于智能家居的纵向功能评估

在本文中，我们研究了从智能家居传感器数据执行自动认知健康评估的方法。具体来说，我们引入了一种算法，使用纵向智能家居传感器数据来量化和跟踪智能家居居民日常生活活动和移动性随时间的变化。我们使用自动活动识别算法从生成的传感器数据中识别智能家居居民的日常生活活动，并引入比较和计数(2C)算法来量化日常行为的变化。我们使用从18个单居民智能家庭收集的纵向传感器数据集测试了我们的方法，该数据集收集了近两年的时间，并研究了基于传感器的日常功能参数的观察变化与标准临床健康评估分数变化之间的关系。结果表明，我们可能能够开发基于传感器的变化算法，可以预测认知和身体健康的特定组成部分。

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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Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

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