Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study

Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study

BackgroundThe emergence of smartphones, wearable sensor technologies, and smart homes allows the nonintrusive collection of activity data. Thus, health-related events, such as activities of daily living (ADLs; eg, mobility patterns, feeding, sleeping, ...) can be captured without patients’ active pa...

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Bibliographic Details
Main Authors: Berrouiguet, Sofian, Ramírez, David, Barrigón, María Luisa, Moreno-Muñoz, Pablo, Carmona Camacho, Rodrigo, Baca-García, Enrique, Artés-Rodríguez, Antonio
Format: Article
Language:English
Published: JMIR Publications 2018-12-01
Series:JMIR mHealth and uHealth
Online Access:https://mhealth.jmir.org/2018/12/e197/

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https://mhealth.jmir.org/2018/12/e197/

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