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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/4/1476 |
| _version_ | 1797476714669932544 |
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| author | Shibo Zhang Yaxuan Li Shen Zhang Farzad Shahabi Stephen Xia Yu Deng Nabil Alshurafa |
| author_facet | Shibo Zhang Yaxuan Li Shen Zhang Farzad Shahabi Stephen Xia Yu Deng Nabil Alshurafa |
| author_sort | Shibo Zhang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T21:06:35Z |
| format | Article |
| id | doaj.art-d840901d624a4fde972fc3373097c100 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:06:35Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d840901d624a4fde972fc3373097c1002023-11-23T22:00:09ZengMDPI AGSensors1424-82202022-02-01224147610.3390/s22041476Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on AdvancesShibo Zhang0Yaxuan Li1Shen Zhang2Farzad Shahabi3Stephen Xia4Yu Deng5Nabil Alshurafa6Department of Computer Science, McCormick School of Engineering, Northwestern University, Mudd Hall, 2233 Tech Drive, Evanston, IL 60208, USAElectrical and Computer Engineering Department, McGill University, McConnell Engineering Building, 3480 Rue University, Montréal, QC H3A 0E9, CanadaSchool of Electrical and Computer Engineering, Georgia Institute of Technology, 777 Atlantic Drive, Atlanta, GA 30332, USADepartment of Computer Science, McCormick School of Engineering, Northwestern University, Mudd Hall, 2233 Tech Drive, Evanston, IL 60208, USADepartment of Electrical Engineering, Columbia University, Mudd 1310, 500 W. 120th Street, New York, NY 10027, USACenter for Health Information Partnerships, Feinberg School of Medicine, Northwestern University, 625 N Michigan Ave, Chicago, IL 60611, USADepartment of Computer Science, McCormick School of Engineering, Northwestern University, Mudd Hall, 2233 Tech Drive, Evanston, IL 60208, USAMobile 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.https://www.mdpi.com/1424-8220/22/4/1476reviewhuman activity recognitiondeep learningwearable sensorsubiquitous computingpervasive computing |
| spellingShingle | Shibo Zhang Yaxuan Li Shen Zhang Farzad Shahabi Stephen Xia Yu Deng Nabil Alshurafa Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances Sensors review human activity recognition deep learning wearable sensors ubiquitous computing pervasive computing |
| title | Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances |
| title_full | Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances |
| title_fullStr | Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances |
| title_full_unstemmed | Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances |
| title_short | Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances |
| title_sort | deep learning in human activity recognition with wearable sensors a review on advances |
| topic | review human activity recognition deep learning wearable sensors ubiquitous computing pervasive computing |
| url | https://www.mdpi.com/1424-8220/22/4/1476 |
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