Automated Cognitive Health Assessment Based on Daily Life Functional Activities

Abstract

Dementia is increasing day-by-day in older adults. Many of them are spending their life joyfully due to smart home technologies. Smart homes contain several smart devices which can support living at home. Automated assessment of smart home residents is a significant aspect of smart home technology. Detecting dementia in older adults in the early stage is the basic need of this time. Existing technologies can detect dementia timely but lacks performance. In this paper, we proposed an automated cognitive health assessment approach using machines and deep learning based on daily life activities. To validate our approach, we use CASAS publicly available daily life activities dataset for experiments where residents perform their routine activities in a smart home. We use four machine learning algorithms: decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and multilayer perceptron (MLP). Furthermore, we use deep neural network (DNN) for healthy and dementia classification. Experiments reveal the 96 % accuracy using the MLP classifier. This study suggests using machine learning classifiers for better dementia detection, specifically for the dataset which contains real-world data.

A Semantic Framework to Detect Problems in Activities of Daily Living Monitored through Smart Home Sensors

“…Passive remote monitoring systems have the potential nowadays to collect data for longitudinal research on daily activity, cognitive function, or treatment response. Smart home technology, as proposed in a recent literature review, monitors both cognitive functions and ADLs in a simulated home environment [ 9 ], while the installed sensors can detect potential risks and enhance remote clinical assessment [ 10 ]. Therefore, adapting assistive technologies like smart homes to support independent living for people with dementia or early stages such as mild cognitive impairment (MCI), offering applications for memory aid, executive functions, social interactions, and more, constitutes a challenging recent approach [ 11 ].…”