AutoCogniSys: IoT Assisted Context-Aware Automatic Cognitive Health Assessment

被引：6

作者：

Ul Alam, Mohammad Arif ^{[1
]}

Roy, Nirmalya ^{[2
]}

Holmes, Sarah ^{[3
,4
]}

Gangopadhyay, Aryya ^{[2
]}

Galik, Elizabeth ^{[5
]}

机构：

[1] Univ Massachusetts Lowell, Dept Comp Sci, Lowell, MA 01854 USA

[2] Univ Maryland Baltimore Cty, Dept Informat Syst, Baltimore, MD 21228 USA

[3] Univ Maryland, Dept Gerontol, Baltimore, MD 21201 USA

[4] Univ Maryland Baltimore Cty, Dept Gerontol, Baltimore, MD 21228 USA

[5] Univ Maryland, Sch Nursing, Baltimore, MD 21201 USA

来源：

PROCEEDINGS OF THE 17TH EAI INTERNATIONAL CONFERENCE ON MOBILE AND UBIQUITOUS SYSTEMS: COMPUTING, NETWORKING AND SERVICES (MOBIQUITOUS 2020) | 2021年

关键词：

internet of things; signal processing; machine learning; cognitive assessment; activity recognition; ARTIFACTS; REMOVAL;

D O I：

10.1145/3448891.3448945

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：184 / 195

页数：12

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