# 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. ambient assisted living; binary sensor network; unsupervised behavior modeling; sleep quality analysis; outing/visiting detection; behavior pattern extraction; anomaly detection +
Footnote †: journal: Future Internet
technologies that supports 'aging in place', where elderly can live independently and safely at the comfort of their own homes for as long as possible, have been proposed in recent years to mitigate the rising healthcare costs [2]. The goal is to establish systems to report on health status objectively and automatically, informing caregivers about subject habits, trends and critical conditions. Presently, most geriatric methods rely on manual administration and on internationally validated questionnaires designed for different medical purposes, to diagnose the physical and cognitive condition of a patient. In practice, such investigations are often carried out through home visits by healthcare professionals, or hospital visits by patients. This approach is labor intense, results in sparse health state reports and thus, is not an appropriate solution for the growing elderly population and their need for long-term monitoring. In particular, when a high visiting frequency (e.g., on a daily basis) is required. Additionally, the response collected through self-reported questionnaires does not fully reflect the objective medical condition of the patients. It is challenging for patients, especially elderly patients, to recall all past events at assessment time, thus early signs of behavior change are often missed. Researchers have found that between 29% and 76% of age-related cognitive impairment are difficult to be detected and assessed in the brief time available during an office visit [3].