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Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Shibo ZhangDepartment of Computer Science, McCormick School of Engineering, Northwestern University, Mudd Hall, 2233 Tech Drive, Evanston, IL 60208, USAShibo ZhangDepartment of Computer Science, McCormick School of Engineering, Northwestern University, Mudd Hall, 2233 Tech Drive, Evanston, IL 60208, USAYaxuan LiElectrical and Computer Engineering Department, McGill University, McConnell Engineering Building, 3480 Rue University, Montréal, QC H3A 0E9, CanadaShen ZhangDepartment of Computer Science, McCormick School of Engineering, Northwestern University, Mudd Hall, 2233 Tech Drive, Evanston, IL 60208, USAFarzad ShahabiDepartment of Computer Science, McCormick School of Engineering, Northwestern University, Mudd Hall, 2233 Tech Drive, Evanston, IL 60208, USAStephen XiaDepartment of Electrical Engineering, Columbia University, Mudd 1310, 500 W. 120th Street, New York, NY 10027, USAYu DengCenter for Health Information Partnerships, Feinberg School of Medicine, Northwestern University, 625 N Michigan Ave, Chicago, IL 60611, USANabil AlshurafaDepartment of Computer Science, McCormick School of Engineering, Northwestern University, Mudd Hall, 2233 Tech Drive, Evanston, IL 60208, USA

2022en

ABI

Annotatsiya

Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human-computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR.

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DOI: 10.3390/s22041476

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