Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB(2)) Study

BACKGROUND: The 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...

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Detalles Bibliográficos
Autores principales: Berrouiguet, Sofian, Ramírez, David, Barrigón, María Luisa, Moreno-Muñoz, Pablo, Carmona Camacho, Rodrigo, Baca-García, Enrique, Artés-Rodríguez, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2018
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6305880/
https://www.ncbi.nlm.nih.gov/pubmed/30530465
http://dx.doi.org/10.2196/mhealth.9472

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6305880/
https://www.ncbi.nlm.nih.gov/pubmed/30530465
http://dx.doi.org/10.2196/mhealth.9472

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