Edge Computing-Based Self-Organized Device Network for Awareness Activities of Daily Living in the Home

Abstract

Activities of daily living (ADL) are important indicators for awareness of brain health in the elderly, and hospitals use ADL as a standard test for diagnosing chronic brain diseases such as dementia. However, since it is difficult to judge real-life ADL in hospitals, doctors typically predict ADL ability through interviews with patients or accompanying caregivers. Recently, many studies have attempted to diagnose accurate brain health by collecting and analyzing the real-life ADL of patients in their living environments. However, most of these were conducted by constructing and implementing expensive smart homes with the concept of centralized computing, and ADL data were collected from simple data about patients’ home appliance usage and the surrounding environment. Despite the high cost of building a smart home, the collected ADL data are inadequate for predicting accurate brain health. In this study, we developed and used three types of portable devices (wearable, tag, and stationary) that can be easily installed and operated in typical existing houses. We propose a self-organized device network structure based on edge computing that can perform user perception, location perception, and behavioral perception simultaneously. This approach enables us to collect user activity data, analyze ADL in real-time to determine if the user’s behavior was successful or abnormal, and record the physical ability of the user to move between fixed spaces. The characteristics of this proposed system enable us to distinguish patients from other family members and provide real-time notifications after a forgetful or mistaken action. We implemented devices that constitute the edge network of the smart home scenario and evaluated the performance of this system to verify its usefulness.

Intelligence Complements from the Built Environment: A Review of Cps-Enabled Smart Buildings for Cognitively Declined Occupants

“…Activity recognition, or often times known as plan recognition, aims to comprehend occupants' actions and intentions through performing ADLs [60]. In behavioral monitoring, the Processing Layer evaluates the performance of the occupants performing various activities and identify patterns of behavior [61], or perform both the activity recognition and anomaly detection [6].…”

“…The identified solutions and services are then delivered to the occupants through either prompts or interventions. While the majority of the articles targeted a cognitively impaired demographic group in general [40,57,58,[62][63][64][65][66], others carried out their studies specifically on a sub-category of cognitive impairment such as traumatic brain injury (TBI) [67,68], dementia [56,59,61,[69][70][71], stroke rehabilitation [56], Alzheimer Disease [6,9], Parkinson Disease [72], Apraxia, and action disorganization syndrome [73].…”

“…Moreover, they employed Recurrent Neural Networks (RNN) to detect the Freeze of Gait (FoG). Keum et al [61] proposed a system that constructed behavior patterns based on analyzing the sequential timing of single activities to recognize abnormalities in ADLs using edge computing. Enshaeifar et al [85] developed two algorithms to detect Urinary Tract Infections (UTI) and changes in activity patterns of the occupants to provide personalized assistive services using a Non-negative Matrix Factorization (NMF) and an Isolation Forest (iForest) technique.…”