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A Review and Taxonomy of Activity Recognition on Mobile Phones

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Abstract

The release of smart phones equipped with a rich set of sensors has enabled human activity recognition on mobile platforms. Monitoring the daily activities and their levels helps in recognizing the health and wellness of the users as a practical application. Mobile phone’s ubiquity, unobtrusiveness, ease of use, communication channels, and playfulness make mobile phones a suitable platform also for inducing behavior change for a healthier and more active lifestyle. In this paper, we provide a review on the activity recognition systems that use integrated sensors in the mobile phone with a special focus on the systems that target personal health and well-being applications. Initially, we provide background information about the activity recognition process, such as the sensors used, activities targeted, and the steps of activity recognition using machine learning algorithms, before listing the challenges of activity recognition on mobile phones. Next, we focus on the classification of existing work on the topic together with a detailed taxonomy. Finally, we investigate the directions for future research.

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Notes

  1. We admit that camera-based systems are more powerful in detecting a more detailed context for the user, and phone-based activity recognition may not replace the camera-based solutions in settings where each action is required to be detected. Here, our consideration is about the set of the activities that can be detected with the set of sensors available on the phones, and the camera still can be used for detailed context recognition.

  2. Although activity recognition can be performed using rule-based inference or using unsupervised techniques, it can be very challenging to discriminate activities in this context [54].

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  1. Department of Computer Engineering, Bogazici University, Bebek, 34342, Istanbul, Turkey

    Ozlem Durmaz Incel, Mustafa Kose & Cem Ersoy

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Correspondence to Ozlem Durmaz Incel.

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This work is supported by the Turkish State Planning Organization (DPT) under the TAM Project number 2007K120610 and by Bogazici University Research Fund under grant agreement number “6056”.

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Incel, O.D., Kose, M. & Ersoy, C. A Review and Taxonomy of Activity Recognition on Mobile Phones. BioNanoSci. 3, 145–171 (2013). https://doi.org/10.1007/s12668-013-0088-3

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