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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. https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16/ http://schema.org/encodingFormat application/ld+json https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16/ http://schema.org/hasPart https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16/resources/1e2da040-31b3-4194-a6a4-38d48959e0f4 https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16/ http://schema.org/identifier https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16 https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16/ http://schema.org/license https://choosealicense.com/no-permission/ https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16/ http://schema.org/name An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16/ 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"An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16. https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16/resources/1e2da040-31b3-4194-a6a4-38d48959e0f4 http://schema.org/author mailto:service-account-generation-service https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16/resources/1e2da040-31b3-4194-a6a4-38d48959e0f4 http://schema.org/contentSize 261 https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16/resources/1e2da040-31b3-4194-a6a4-38d48959e0f4 http://schema.org/contentUrl https://api.rohub.org/api/resources/1e2da040-31b3-4194-a6a4-38d48959e0f4/download/ https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16/resources/1e2da040-31b3-4194-a6a4-38d48959e0f4 http://schema.org/creator mailto:service-account-generation-service https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16/resources/1e2da040-31b3-4194-a6a4-38d48959e0f4 http://schema.org/dateCreated 2021-12-10 09:57:27.302384+00:00 https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16/resources/1e2da040-31b3-4194-a6a4-38d48959e0f4 http://schema.org/dateModified 2021-12-10 09:57:27.303841+00:00 https://w3id.org/ro-id/bf8eb7b1-21d6-4a42-b574-23a4279e5f16/resources/1e2da040-31b3-4194-a6a4-38d48959e0f4 http://schema.org/description 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. 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