  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.
