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Human Behavior Analysis by Means of Multimodal Context Mining

There is sufficient evidence proving the impact that negative lifestyle choices have on people’s health and wellness. Changing unhealthy behaviours requires raising people’s self-awareness and also providing healthcare experts with a thorough and continuous description of the user’s conduct. Several...

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Autores principales: Banos, Oresti, Villalonga, Claudia, Bang, Jaehun, Hur, Taeho, Kang, Donguk, Park, Sangbeom, Huynh-The, Thien, Le-Ba, Vui, Amin, Muhammad Bilal, Razzaq, Muhammad Asif, Khan, Wahajat Ali, Hong, Choong Seon, Lee, Sungyoung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017429/
https://www.ncbi.nlm.nih.gov/pubmed/27517928
http://dx.doi.org/10.3390/s16081264
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author Banos, Oresti
Villalonga, Claudia
Bang, Jaehun
Hur, Taeho
Kang, Donguk
Park, Sangbeom
Huynh-The, Thien
Le-Ba, Vui
Amin, Muhammad Bilal
Razzaq, Muhammad Asif
Khan, Wahajat Ali
Hong, Choong Seon
Lee, Sungyoung
author_facet Banos, Oresti
Villalonga, Claudia
Bang, Jaehun
Hur, Taeho
Kang, Donguk
Park, Sangbeom
Huynh-The, Thien
Le-Ba, Vui
Amin, Muhammad Bilal
Razzaq, Muhammad Asif
Khan, Wahajat Ali
Hong, Choong Seon
Lee, Sungyoung
author_sort Banos, Oresti
collection PubMed
description There is sufficient evidence proving the impact that negative lifestyle choices have on people’s health and wellness. Changing unhealthy behaviours requires raising people’s self-awareness and also providing healthcare experts with a thorough and continuous description of the user’s conduct. Several monitoring techniques have been proposed in the past to track users’ behaviour; however, these approaches are either subjective and prone to misreporting, such as questionnaires, or only focus on a specific component of context, such as activity counters. This work presents an innovative multimodal context mining framework to inspect and infer human behaviour in a more holistic fashion. The proposed approach extends beyond the state-of-the-art, since it not only explores a sole type of context, but also combines diverse levels of context in an integral manner. Namely, low-level contexts, including activities, emotions and locations, are identified from heterogeneous sensory data through machine learning techniques. Low-level contexts are combined using ontological mechanisms to derive a more abstract representation of the user’s context, here referred to as high-level context. An initial implementation of the proposed framework supporting real-time context identification is also presented. The developed system is evaluated for various realistic scenarios making use of a novel multimodal context open dataset and data on-the-go, demonstrating prominent context-aware capabilities at both low and high levels.
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spelling pubmed-50174292016-09-22 Human Behavior Analysis by Means of Multimodal Context Mining Banos, Oresti Villalonga, Claudia Bang, Jaehun Hur, Taeho Kang, Donguk Park, Sangbeom Huynh-The, Thien Le-Ba, Vui Amin, Muhammad Bilal Razzaq, Muhammad Asif Khan, Wahajat Ali Hong, Choong Seon Lee, Sungyoung Sensors (Basel) Article There is sufficient evidence proving the impact that negative lifestyle choices have on people’s health and wellness. Changing unhealthy behaviours requires raising people’s self-awareness and also providing healthcare experts with a thorough and continuous description of the user’s conduct. Several monitoring techniques have been proposed in the past to track users’ behaviour; however, these approaches are either subjective and prone to misreporting, such as questionnaires, or only focus on a specific component of context, such as activity counters. This work presents an innovative multimodal context mining framework to inspect and infer human behaviour in a more holistic fashion. The proposed approach extends beyond the state-of-the-art, since it not only explores a sole type of context, but also combines diverse levels of context in an integral manner. Namely, low-level contexts, including activities, emotions and locations, are identified from heterogeneous sensory data through machine learning techniques. Low-level contexts are combined using ontological mechanisms to derive a more abstract representation of the user’s context, here referred to as high-level context. An initial implementation of the proposed framework supporting real-time context identification is also presented. The developed system is evaluated for various realistic scenarios making use of a novel multimodal context open dataset and data on-the-go, demonstrating prominent context-aware capabilities at both low and high levels. MDPI 2016-08-10 /pmc/articles/PMC5017429/ /pubmed/27517928 http://dx.doi.org/10.3390/s16081264 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Banos, Oresti
Villalonga, Claudia
Bang, Jaehun
Hur, Taeho
Kang, Donguk
Park, Sangbeom
Huynh-The, Thien
Le-Ba, Vui
Amin, Muhammad Bilal
Razzaq, Muhammad Asif
Khan, Wahajat Ali
Hong, Choong Seon
Lee, Sungyoung
Human Behavior Analysis by Means of Multimodal Context Mining
title Human Behavior Analysis by Means of Multimodal Context Mining
title_full Human Behavior Analysis by Means of Multimodal Context Mining
title_fullStr Human Behavior Analysis by Means of Multimodal Context Mining
title_full_unstemmed Human Behavior Analysis by Means of Multimodal Context Mining
title_short Human Behavior Analysis by Means of Multimodal Context Mining
title_sort human behavior analysis by means of multimodal context mining
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017429/
https://www.ncbi.nlm.nih.gov/pubmed/27517928
http://dx.doi.org/10.3390/s16081264
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