A Review and Taxonomy of Activity Recognition on Mobile Phones

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.