Abstract
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.
Highlights
The last World Health Organization (WHO) global status report on noncommunicable diseases reveals that illnesses associated with lifestyle choices are currently the leading cause of death worldwide [1]
Level Context Awareness (LLCA) is in charge of converting the wide-spectrum of data obtained from the user interaction with the real and cyber world into abstract concepts or categories, namely physical activities, emotional states and locations
In order to search for the concurrent low-level contexts, the Context Synchronizer requests information stored in the Context Manager and accesses it through the Context Instance Handler
Summary
The last World Health Organization (WHO) global status report on noncommunicable diseases reveals that illnesses associated with lifestyle choices are currently the leading cause of death worldwide [1]. This paper further extends prior work [20,21,22] while providing an exhaustive evaluation of the potential and validity of the proposed solution In this sense, this paper presents a more functional and applied perspective of the low-high level context dualism, as well as the mechanisms to obtain one out of the other. This paper presents a more functional and applied perspective of the low-high level context dualism, as well as the mechanisms to obtain one out of the other It goes beyond prior contributions, which mainly elaborated on accelerometer data to infer the user’s physical activity in order to describe their behaviour.
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