Seyed Amir Hoseini-Tabatabaei
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Abstract
The ever-growing computation and storage capability of mobile phones have given rise to mobile-centric context recognition systems, which are able to sense and analyze the context of the carrier so as to provide an appropriate level of service. As nonintrusive autonomous sensing and context recognition are desirable characteristics of a personal sensing system; efforts have been made to develop opportunistic sensing techniques on mobile phones. The resulting combination of these approaches has ushered in a new realm of applications, namely opportunistic user context recognition with mobile phones.
This article surveys the existing research and approaches towards realization of such systems. In doing so, the typical architecture of a mobile-centric user context recognition system as a sequential process of sensing, preprocessing, and context recognition phases is introduced. The main techniques used for the realization of the respective processes during these phases are described, and their strengths and limitations are highlighted. In addition, lessons learned from previous approaches are presented as motivation for future research. Finally, several open challenges are discussed as possible ways to extend the capabilities of current systems and improve their real-world experience.
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Index Terms
- A survey on smartphone-based systems for opportunistic user context recognition
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Reviews
Angelica de Antonio
This comprehensive and detailed review of existing approaches and techniques for opportunistic user context recognition concentrates on methods that operate autonomously. The approach proposed in this paper focuses on mobile phone-centric methodologies. The analysis follows three main process stages: sensing, preprocessing, and context recognition. For the sensing stage, the authors explore three main types of sensors-inertial, positioning, and ambient-and describe how they work, the types of information they can provide, their strengths and weaknesses, and how they have been applied in context recognition systems for mobile phones. The section devoted to preprocessing presents different approaches for filtering and converting raw sensor data into a finite set of features for further analysis. After a discussion of calibration methods to address the effects of variable device position and orientation on the measurements provided by sensors, the authors describe the types of features that can be generated from raw data (time domain, frequency domain, and heuristic features), organized around the types of contexts that can be inferred from them (user physical activity, social interactions, and environment). Finally, the classification algorithms that have been employed to infer the user context from the input features on mobile devices are analyzed. Five discriminative models and three generative models are described; their advantages, drawbacks, and challenges are highlighted. The paper closes with a reflective comparison of different approaches and the identification of future research challenges and recommendations. Extensive references to relevant literature appear throughout the survey, making this paper an excellent overview of the field and a perfect starting point for readers interested in exploring it in depth. Online Computing Reviews Service
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