An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care

Abstract

Artificial Intelligence in combination with the Internet of Medical Things enables remote healthcare services through networks of environmental and/or personal sensors. We present a remote healthcare service system which collects real-life data through an environmental sensor package, including binary motion, contact, pressure, and proximity sensors, installed at households of elderly people. Its aim is to keep the caregivers informed of subjects’ health-status progressive trajectory, and alert them of health-related anomalies to enable objective on-demand healthcare service delivery at scale. The system was deployed in 19 households inhabited by an elderly person with post-stroke condition in the Emilia–Romagna region in Italy, with maximal and median observation durations of 98 and 55 weeks. Among these households, 17 were multi-occupancy residences, while the other 2 housed elderly patients living alone. Subjects’ daily behavioral diaries were extracted and registered from raw sensor signals, using rule-based data pre-processing and unsupervised algorithms. Personal behavioral habits were identified and compared to typical patterns reported in behavioral science, as a quality-of-life indicator. We consider the activity patterns extracted across all users as a dictionary, and represent each patient’s behavior as a ‘Bag of Words’, based on which patients can be categorized into sub-groups for precision cohort treatment. Longitudinal trends of the behavioral progressive trajectory and sudden abnormalities of a patient were detected and reported to care providers. Due to the sparse sensor setting and the multi-occupancy living condition, the sleep profile was used as the main indicator in our system. Experimental results demonstrate the ability to report on subjects’ daily activity pattern in terms of sleep, outing, visiting, and health-status trajectories, as well as predicting/detecting 75% hospitalization sessions up to 11 days in advance. 65% of the alerts were confirmed to be semantically meaningful by the users. Furthermore, reduced social interaction (outing and visiting), and lower sleep quality could be observed during the COVID-19 lockdown period across the cohort.

Construction of a Digital Elderly Care Service System Based on Human-Computer Interaction from the Perspective of Smart Elderly Care

“…Hu et al proposed a telemedicine service system. Based on human-computer interaction technology, the system collects real-life data through an environmental sensor package (including binary motion, contact, pressure, and proximity sensors) installed in the homes of the elderly [8]. However, these studies are not practical enough.…”

A Review of Abnormal Behavior Detection in Activities of Daily Living

“…The authors [26] used wellness indices which include the user's wellbeing, movement, and emotion level or rules [33] to determine if the activity is abnormal. [28], [35], [59] produced synthetic abnormal behaviors based on three factors: frequency, order, and time taken to perform an activity. An abnormal behavior flag is raised if any activity hits on the threshold set for these factors.…”

Development of an Anomaly Alert System Triggered by Unusual Behaviors at Home

“…In addition to these studies, many other studies are focused on WSN systems, in which ambient sensors are placed on one or more objects and in rooms around the house, in order to record the interaction that the older person has with these objects throughout the day (pressure-based bed and/or chair sensors, contact-based door sensors, proximity-based toilet sensors, motion sensors, etc.) [35][36][37][38][39][40][41][42][43][44]; however, this option generally requires a significant installation of sensors in different points around the house, which can cause insecurities in the elderly, making them feel uncomfortable or invaded in their own home. For this reason, the device for the home that we have developed incorporates a wireless sensor network in a single device, which is intended to be placed in the room most used by the user.…”