论文标题
自闭症：IoT辅助上下文感知自动认知健康评估
AutoCogniSys: IoT Assisted Context-Aware Automatic Cognitive Health Assessment
论文作者
论文摘要
认知障碍已成为老年人的流行病。又名物联网（IoT）最近出现了微小的可穿戴设备（IoT），为老年人的连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，实施和评估自动认知，一种自动化的认知健康评估系统，结合了可穿戴生理（电甲状化活性，光插图学）和物理计（加速度计，对象）传感器的传感能力与环境传感器。我们设计适当的信号处理和机器学习技术，并在天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了我们的方法：（i）自然主义的传感器数据流与在退休社区中心招募的22名老年人的日常生活和精神唤醒有关，使用定制的廉价IoT系统（IRB＃HP-00064387）和（ii）公开可用的数据集对情感数据集中招募的公寓（IRB＃HP-00064387），单独生活在自己的公寓中。自闭症的表现证明了最大。 93 \评估老年人认知健康的准确性。
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.
