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The ubiquity of smartphones together with their ever-growing computing, networking, and sensing powers have been changing the landscape of people's daily life. Among others, activity recognition, which takes the raw sensor reading as inputs and predicts a user's motion activity, has become an active research area in recent years. It is the core building block in many high-impact applications, ranging from health and fitness monitoring, personal biometric signature, urban computing, assistive technology, and elder-care, to indoor localization and navigation, etc. This paper presents a comprehensive survey of the recent advances in activity recognition with smartphones' sensors. We start with the basic concepts such as sensors, activity types, etc. We review the core data mining techniques behind the main stream activity recognition algorithms, analyze their major challenges, and introduce a variety of real applications enabled by activity recognition.
Su et al. (Sun,) studied this question.