# AutoCogniSys: IoT Assisted Context-Aware Automatic Cognitive Health Assessment

arXiv: https://arxiv.org/abs/2003.07492 · HTML: https://mlsanity.com/paper/2003.07492/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Sustainability Through Cognition Aware Safety Systems -- Next Level Human-Machine-Interaction](https://mlsanity.com/paper/2110.07003/) (2021)
- [PhysioGait: Context-Aware Physiological Context Modeling for Person Re-identification Attack on Wearable Sensing](https://mlsanity.com/paper/2211.02622/) (2022)

## Abstract (arXiv)

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.