Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Northwestern University · Georgia Institute of Technology · +2 more institutions
Abstract
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…
Citation impact
- FWCI
- 42.73
- Percentile
- 100%
- References
- 350
Authors
9- SZShibo Zhang
Northwestern University, Georgia Institute of Technology
- SZShibo ZhangCorresponding
Northwestern University, Georgia Institute of Technology, McGill University
- YLYaxuan Li
Georgia Institute of Technology, McGill University
- SZShen Zhang
Northwestern University, Georgia Institute of Technology
- SZShen ZhangCorresponding
Northwestern University, Georgia Institute of Technology, Columbia University
Topics & keywords
- Wearable computer
- Activity recognition
- Deep learning
- Human–computer interaction
- Wearable technology
- Computer science
- Artificial intelligence
- Embedded system