Context-aware multi-inhabitant functional and physiological health assessment in smart home environment

M. A. U. Alam
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引用次数: 12

Abstract

Recognizing the human activity, behavior and physiological symptoms in smart home environments is of utmost importance for the functional, physiological and cognitive health assessment of the older adults. Unprecedented data from everyday devices such as smart wristbands, smart ornaments, smartphones, and ambient sensors provide opportunities for activity mining and inference, but pose fundamental research challenges in data processing, physiological feature extraction, activity learning and inference in the presence of multiple inhabitants. In this thesis, we develop micro-activity driven macro-activity recognition approaches while considering the underpinning spatiotemporal constraints and correlations across multiple inhabitants. We design novel signal processing and machine learning algorithms to detect physiological symptoms and infer macro-level activity of the inhabitants, respectively. We combine these activity recognition methodologies along with the physiological health assessment approaches to quantify the functional, behavioral, and cognitive health of the older adults. real-time data collected data from a continuing care retirement community center (IRB #HP-00064387) helped us to evaluate, compare, and benchmark our proposed computational approaches with the clinical tools used extensively for functional and cognitive health assessment.
智能家居环境中情境感知的多居民功能和生理健康评估
识别智能家居环境中的人类活动、行为和生理症状对于老年人的功能、生理和认知健康评估至关重要。来自智能手环、智能饰品、智能手机和环境传感器等日常设备的前所未有的数据为活动挖掘和推理提供了机会,但在数据处理、生理特征提取、活动学习和推理等方面提出了基础研究挑战。在本文中,我们开发了微活动驱动的宏观活动识别方法,同时考虑了多个居民之间的基础时空约束和相关性。我们设计了新的信号处理和机器学习算法,分别检测生理症状和推断居民的宏观活动。我们将这些活动识别方法与生理健康评估方法结合起来,量化老年人的功能、行为和认知健康。从持续护理退休社区中心(irb# HP-00064387)收集的实时数据帮助我们评估、比较和基准我们提出的计算方法与广泛用于功能和认知健康评估的临床工具。
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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