AutoCogniSys: IoT Assisted Context-Aware Automatic Cognitive Health Assessment

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2003.07492.pdf (3.01 MB)

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https://arxiv.org/abs/2003.07492

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http://hdl.handle.net/11603/18034

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UMBC Information Systems Department
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2020-03-17

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journal articles preprints
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Alam, Mohammad Arif Ul; Roy, Nirmalya; Holmes, Sarah; Gangopadhyay, Aryya; Galik, Elizabeth; AutoCogniSys: IoT Assisted Context-Aware Automatic Cognitive Health Assessment; Human-Computer Interaction (2020); https://arxiv.org/abs/2003.07492

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Abstract

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.