Activity recognition with smartphone sensors

By Xing Su, Hanghang Tong, Ping Ji. 2014

375 citations recorded by the source corpus.

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 recoginition, 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.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Transportation

View Paper: https://www.semanticscholar.org/paper/6057f0221d3366f04ece9e0f20206da70694a1c5

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In Computer Networks and Communications

In Computer Vision and Pattern Recognition

In Transportation

Citations in the corpus

Scaling Human Activity Recognition via Deep Learning-based Domain Adaptation (2018, background): “, for inferring human activity and its correlation with behavioral health [11], [1], [12].”
Smart Devices are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition (2015, background): “Motion sensors can be further paired with sensors, e.g., GPS, GSM, WiFi, and barometer, especially for recognizing tasks beyond basic HAR.”
A Survey of Online Activity Recognition Using Mobile Phones (2015, background): “There is a number of survey publications that have reviewed the work done so far in this area [1,14–17].”