Human Behavior Analysis by Means of Multimodal Context Mining

Sensors

Sangbeom Park¹  Claudia Villalonga¹  Muhammad Bilal Amin¹  Sungyoung Lee¹  Choong Seon Hong¹  Muhammad Asif Razzaq¹  Wahajat Ali Khan¹  Vui Le-Ba¹  Thien Huynh-The¹  Oresti Banos¹  Donguk Kang¹  Taeho Hur¹  Jaehun Bang¹ 

[1] Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea;

关键词: human behaviour;    context awareness;    activity recognition;    location tracking;    emotion identification;    machine learning;    ontologies;   

DOI  :  10.3390/s16081264

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown