Cognitive impairment has become epidemic in older adult population. The recent advent of tiny wearable and ambient devices, a.k.a Internet of Things (IoT) provides ample platforms for continuous functional and cognitive health assessment of older adults. In this paper, we design, implement and evaluate AutoCogniSys, a context-aware automated cognitive health assessment system, combining the sensing powers of wearable physiological (Electrodermal Activity, Photoplethysmography) and physical (Accelerometer, Object) sensors in conjunction with ambient sensors. We design appropriate signal processing and machine learning techniques, and develop an automatic cognitive health assessment system in a natural older adults living environment. We validate our approaches using two datasets: (i) a naturalistic sensor data streams related to Activities of Daily Living and mental arousal of 22 older adults recruited in a retirement community center, individually living in their own apartments using a customized inexpensive IoT system (IRB #HP-00064387) and (ii) a publicly available dataset for emotion detection. The performance of AutoCogniSys attests max. 93\% of accuracy in assessing cognitive health of older adults.

中文翻译：

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AutoCogniSys：物联网辅助上下文感知自动认知健康评估

认知障碍已成为老年人口的流行病。最近出现的微型可穿戴设备和环境设备，即物联网 (IoT)，为老年人的持续功能和认知健康评估提供了充足的平台。在本文中，我们设计、实施和评估了 AutoCogniSys，这是一个上下文感知的自动化认知健康评估系统，结合了可穿戴生理（电皮活动、光电容积描记）和物理（加速度计、物体）传感器与环境传感器的传感能力。我们设计适当的信号处理和机器学习技术，并在自然的老年人生活环境中开发自动认知健康评估系统。我们使用两个数据集验证我们的方法：(i) 在退休社区中心招募的 22 名老年人的日常生活活动和精神唤醒相关的自然传感器数据流，他们使用定制的廉价物联网系统 (IRB #HP-00064387) 单独住在自己的公寓中 (IRB #HP-00064387) 和 (ii) ) 用于情绪检测的公开数据集。AutoCogniSys 的性能证明了最大。评估老年人认知健康的准确度为 93%。