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Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
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 man...
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Published in: | Sensors (Basel, Switzerland) Switzerland), 2022-02, Vol.22 (4), p.1476 |
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description | 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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subjects | Activities of daily living Algorithms Automobile industry Computational linguistics Computer network equipment industry Datasets Deep Learning Health aspects Human Activities Human activity recognition Human-computer interaction Humans Keywords Language processing Moving object recognition Natural language interfaces Neural networks pervasive computing Physiology Review Sensors Smartwatches Systematic review ubiquitous computing Wearable computers Wearable Electronic Devices wearable sensors Wearable technology |
title | Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances |
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