Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey
In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9257355/ |
| _version_ | 1818617083171176448 |
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| author | Florenc Demrozi Graziano Pravadelli Azra Bihorac Parisa Rashidi |
| author_facet | Florenc Demrozi Graziano Pravadelli Azra Bihorac Parisa Rashidi |
| author_sort | Florenc Demrozi |
| collection | DOAJ |
| description | In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors. |
| first_indexed | 2024-12-16T17:00:03Z |
| format | Article |
| id | doaj.art-270723fbb3c54131bc87773cbd9796a1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:00:03Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-270723fbb3c54131bc87773cbd9796a12022-12-21T22:23:46ZengIEEEIEEE Access2169-35362020-01-01821081621083610.1109/ACCESS.2020.30377159257355Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive SurveyFlorenc Demrozi0https://orcid.org/0000-0002-5422-9826Graziano Pravadelli1https://orcid.org/0000-0002-7833-1673Azra Bihorac2https://orcid.org/0000-0002-5745-2863Parisa Rashidi3https://orcid.org/0000-0003-4530-2048Department of Computer Science, University of Verona, Verona, ItalyDepartment of Computer Science, University of Verona, Verona, ItalyDivision of Nephrology, Hypertension, and Renal Transplantation, College of Medicine, University of Florida, Gainesville, FL, USADepartment of Biomedical Engineering, University of Florida, Gainesville, FL, USAIn the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.https://ieeexplore.ieee.org/document/9257355/Human activity recognition (HAR)deep learning (DL)machine learning (ML)available datasetssensorsaccelerometer |
| spellingShingle | Florenc Demrozi Graziano Pravadelli Azra Bihorac Parisa Rashidi Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey IEEE Access Human activity recognition (HAR) deep learning (DL) machine learning (ML) available datasets sensors accelerometer |
| title | Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey |
| title_full | Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey |
| title_fullStr | Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey |
| title_full_unstemmed | Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey |
| title_short | Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey |
| title_sort | human activity recognition using inertial physiological and environmental sensors a comprehensive survey |
| topic | Human activity recognition (HAR) deep learning (DL) machine learning (ML) available datasets sensors accelerometer |
| url | https://ieeexplore.ieee.org/document/9257355/ |
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