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Open AccessArticle

IoT-Based Home Monitoring: Supporting Practitioners’ Assessment by Behavioral Analysis

1
Università degli Studi di Parma, Dip. Ingegneria e Architettura, Parco Area delle Scienze 181/A, 43124 Parma (PR), Italy
2
WiMonitor S.r.l, Via G. Tacchi 1, 38068 Rovereto (TN), Italy
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National Research Council of Italy, Via Moruzzi 1, 56124 Pisa (PI), Italy
4
IBM Research - Zurich, Saeumerstrasse 4, CH-8803 Rueschlikon, Switzerland
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Azienda Unita’ Sanitaria Locale Di Parma, Str. Quartiere, 2/A, 43125 Parma (PR), Italy
6
Lepida S.c.p.A, Via del Borgo di S. Pietro, 90/c, 40126 Bologna (BO), Italy
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3238; https://doi.org/10.3390/s19143238
Received: 7 June 2019 / Revised: 17 July 2019 / Accepted: 19 July 2019 / Published: 23 July 2019
(This article belongs to the Special Issue IoT Sensors in E-Health)
This paper introduces technical solutions devised to support the Deployment Site - Regione Emilia Romagna (DS-RER) of the ACTIVAGE project. The ACTIVAGE project aims at promoting IoT (Internet of Things)-based solutions for Active and Healthy ageing. DS-RER focuses on improving continuity of care for older adults (65+) suffering from aftereffects of a stroke event. A Wireless Sensor Kit based on Wi-Fi connectivity was suitably engineered and realized to monitor behavioral aspects, possibly relevant to health and wellbeing assessment. This includes bed/rests patterns, toilet usage, room presence and many others. Besides hardware design and validation, cloud-based analytics services are introduced, suitable for automatic extraction of relevant information (trends and anomalies) from raw sensor data streams. The approach is general and applicable to a wider range of use cases; however, for readability’s sake, two simple cases are analyzed, related to bed and toilet usage patterns. In particular, a regression framework is introduced, suitable for detecting trends (long and short-term) and labeling anomalies. A methodology for assessing multi-modal daily behavioral profiles is introduced, based on unsupervised clustering techniques. The proposed framework has been successfully deployed at several real-users’ homes, allowing for its functional validation. Clinical effectiveness will be assessed instead through a Randomized Control Trial study, currently being carried out. View Full-Text
Keywords: IoT; smart home; behavioural analysis; active assisted living (AAL); anomaly detection; continuous monitoring IoT; smart home; behavioural analysis; active assisted living (AAL); anomaly detection; continuous monitoring
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Mora, N.; Grossi, F.; Russo, D.; Barsocchi, P.; Hu, R.; Brunschwiler, T.; Michel, B.; Cocchi, F.; Montanari, E.; Nunziata, S.; Matrella, G.; Ciampolini, P. IoT-Based Home Monitoring: Supporting Practitioners’ Assessment by Behavioral Analysis. Sensors 2019, 19, 3238.

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