Automatic Recognition Methods Supporting Pain Assessment: A Survey
IEEE Automated tools for pain assessment have great promise but have not yet become widely used in clinical practice. In this survey paper, we review the literature that proposes and evaluates automatic pain recognition approaches, and discuss challenges and promising directions for advancing this f...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/136497 |
| _version_ | 1826204340603322368 |
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| author | Werner, Philipp Lopez-Martinez, Daniel Walter, Steffen Al-Hamadi, Ayoub Gruss, Sascha Picard, Rosalind W. |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Werner, Philipp Lopez-Martinez, Daniel Walter, Steffen Al-Hamadi, Ayoub Gruss, Sascha Picard, Rosalind W. |
| author_sort | Werner, Philipp |
| collection | MIT |
| description | IEEE Automated tools for pain assessment have great promise but have not yet become widely used in clinical practice. In this survey paper, we review the literature that proposes and evaluates automatic pain recognition approaches, and discuss challenges and promising directions for advancing this field. Prior to that, we give an overview on pain mechanisms and responses, discuss common clinically used pain assessment tools, and address shared datasets and the challenge of validation in the context of pain recognition. |
| first_indexed | 2024-09-23T12:53:06Z |
| format | Article |
| id | mit-1721.1/136497 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:53:06Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1364972024-08-09T19:55:35Z Automatic Recognition Methods Supporting Pain Assessment: A Survey Werner, Philipp Lopez-Martinez, Daniel Walter, Steffen Al-Hamadi, Ayoub Gruss, Sascha Picard, Rosalind W. Massachusetts Institute of Technology. Media Laboratory IEEE Automated tools for pain assessment have great promise but have not yet become widely used in clinical practice. In this survey paper, we review the literature that proposes and evaluates automatic pain recognition approaches, and discuss challenges and promising directions for advancing this field. Prior to that, we give an overview on pain mechanisms and responses, discuss common clinically used pain assessment tools, and address shared datasets and the challenge of validation in the context of pain recognition. 2021-10-27T20:35:40Z 2021-10-27T20:35:40Z 2019 2021-07-06T13:46:58Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/136497 en 10.1109/TAFFC.2019.2946774 IEEE Transactions on Affective Computing Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Other repository |
| spellingShingle | Werner, Philipp Lopez-Martinez, Daniel Walter, Steffen Al-Hamadi, Ayoub Gruss, Sascha Picard, Rosalind W. Automatic Recognition Methods Supporting Pain Assessment: A Survey |
| title | Automatic Recognition Methods Supporting Pain Assessment: A Survey |
| title_full | Automatic Recognition Methods Supporting Pain Assessment: A Survey |
| title_fullStr | Automatic Recognition Methods Supporting Pain Assessment: A Survey |
| title_full_unstemmed | Automatic Recognition Methods Supporting Pain Assessment: A Survey |
| title_short | Automatic Recognition Methods Supporting Pain Assessment: A Survey |
| title_sort | automatic recognition methods supporting pain assessment a survey |
| url | https://hdl.handle.net/1721.1/136497 |
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