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

Sensors

Yu Deng¹  Shibo Zhang²  Farzad Shahabi²  Nabil Alshurafa²  Stephen Xia³  Yaxuan Li⁴  Shen Zhang⁵ 

[1] Center for Health Information Partnerships, Feinberg School of Medicine, Northwestern University, 625 N Michigan Ave, Chicago, IL 60611, USA;Department of Computer Science, McCormick School of Engineering, Northwestern University, Mudd Hall, 2233 Tech Drive, Evanston, IL 60208, USA;Department of Electrical Engineering, Columbia University, Mudd 1310, 500 W. 120th Street, New York, NY 10027, USA;Electrical and Computer Engineering Department, McGill University, McConnell Engineering Building, 3480 Rue University, Montréal, QC H3A 0E9, Canada;School of Electrical and Computer Engineering, Georgia Institute of Technology, 777 Atlantic Drive, Atlanta, GA 30332, USA;

关键词: review;    human activity recognition;    deep learning;    wearable sensors;    ubiquitous computing;    pervasive computing;   

DOI  :  10.3390/s22041476

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown