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Human Behavior Analysis by Means of Multimodal Context Mining

Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea
Telemedicine Group, Center for Telematics and Information Technology, University of Twente, Enschede 7500AE, The Netherlands
Department of Computer Architecture and Computer Technology, Research Center on Information and Communications Technology, University of Granada, Granada E18071, Spain
Author to whom correspondence should be addressed.
Academic Editors: Vladimir Villarreal and Carmelo R. García
Sensors 2016, 16(8), 1264;
Received: 2 May 2016 / Revised: 12 July 2016 / Accepted: 5 August 2016 / Published: 10 August 2016
(This article belongs to the Special Issue Selected Papers from UCAmI, IWAAL and AmIHEALTH 2015)
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. View Full-Text
Keywords: human behaviour; context awareness; activity recognition; location tracking; emotion identification; machine learning; ontologies human behaviour; context awareness; activity recognition; location tracking; emotion identification; machine learning; ontologies
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MDPI and ACS Style

Banos, O.; Villalonga, C.; Bang, J.; Hur, T.; Kang, D.; Park, S.; Huynh-The, T.; Le-Ba, V.; Amin, M.B.; Razzaq, M.A.; Khan, W.A.; Hong, C.S.; Lee, S. Human Behavior Analysis by Means of Multimodal Context Mining. Sensors 2016, 16, 1264.

AMA Style

Banos O, Villalonga C, Bang J, Hur T, Kang D, Park S, Huynh-The T, Le-Ba V, Amin MB, Razzaq MA, Khan WA, Hong CS, Lee S. Human Behavior Analysis by Means of Multimodal Context Mining. Sensors. 2016; 16(8):1264.

Chicago/Turabian Style

Banos, Oresti, Claudia Villalonga, Jaehun Bang, Taeho Hur, Donguk Kang, Sangbeom Park, Thien Huynh-The, Vui Le-Ba, Muhammad B. Amin, Muhammad A. Razzaq, Wahajat A. Khan, Choong S. Hong, and Sungyoung Lee. 2016. "Human Behavior Analysis by Means of Multimodal Context Mining" Sensors 16, no. 8: 1264.

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