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

Sensors (Basel, Switzerland), 2022-02, Vol.22 (4), p.1476 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2022 by the authors. 2022 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s22041476 ;PMID: 35214377

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Title:
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Author: Zhang, Shibo ; Li, Yaxuan ; Zhang, Shen ; Shahabi, Farzad ; Xia, Stephen ; Deng, Yu ; Alshurafa, Nabil
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
Is Part Of: Sensors (Basel, Switzerland), 2022-02, Vol.22 (4), p.1476
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.
Publisher: Switzerland: MDPI AG
Language: English
Identifier: ISSN: 1424-8220
EISSN: 1424-8220
DOI: 10.3390/s22041476
PMID: 35214377
Source: GFMER Free Medical Journals
MEDLINE
PubMed Central
Coronavirus Research Database
ROAD: Directory of Open Access Scholarly Resources
ProQuest Central
DOAJ Directory of Open Access Journals

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

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