Personalized Automatic Estimation of Self-Reported Pain Intensity from Facial Expressions
© 2017 IEEE. Pain is a personal, subjective experience that is commonly evaluated through visual analog scales (VAS). While this is often convenient and useful, automatic pain detection systems can reduce pain score acquisition efforts in large-scale studies by estimating it directly from the partic...
| 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/1721.1/135725 |
| _version_ | 1826213149427105792 |
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| author | Martinez, Daniel Lopez Rudovic, Ognjen Picard, Rosalind W. |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Martinez, Daniel Lopez Rudovic, Ognjen Picard, Rosalind W. |
| author_sort | Martinez, Daniel Lopez |
| collection | MIT |
| description | © 2017 IEEE. Pain is a personal, subjective experience that is commonly evaluated through visual analog scales (VAS). While this is often convenient and useful, automatic pain detection systems can reduce pain score acquisition efforts in large-scale studies by estimating it directly from the partictipants' facial expressions. In this paper, we propose a novel two-stage learning approach for VAS estimation: first, our algorithm employs Recurrent Neural Networks (RNNs) to automatically estimate Prkachin and Solomon Pain Intensity (PSPI) levels from face images. The estimated scores are then fed into the personalized Hidden Conditional Random Fields (HCRFs), used to estimate the VAS, provided by each person. Personalization of the model is performed using a newly introduced facial expressiveness score, unique for each person. To the best of our knowledge, this is the first approach to automatically estimate VAS from face images. We show the benefits of the proposed personalized over traditional non-personalized approach on a benchmark dataset for pain analysis from face images. |
| first_indexed | 2024-09-23T15:44:06Z |
| format | Article |
| id | mit-1721.1/135725 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:44:06Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1357252024-08-09T20:29:20Z Personalized Automatic Estimation of Self-Reported Pain Intensity from Facial Expressions Martinez, Daniel Lopez Rudovic, Ognjen Picard, Rosalind W. Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Media Laboratory © 2017 IEEE. Pain is a personal, subjective experience that is commonly evaluated through visual analog scales (VAS). While this is often convenient and useful, automatic pain detection systems can reduce pain score acquisition efforts in large-scale studies by estimating it directly from the partictipants' facial expressions. In this paper, we propose a novel two-stage learning approach for VAS estimation: first, our algorithm employs Recurrent Neural Networks (RNNs) to automatically estimate Prkachin and Solomon Pain Intensity (PSPI) levels from face images. The estimated scores are then fed into the personalized Hidden Conditional Random Fields (HCRFs), used to estimate the VAS, provided by each person. Personalization of the model is performed using a newly introduced facial expressiveness score, unique for each person. To the best of our knowledge, this is the first approach to automatically estimate VAS from face images. We show the benefits of the proposed personalized over traditional non-personalized approach on a benchmark dataset for pain analysis from face images. 2021-10-27T20:29:00Z 2021-10-27T20:29:00Z 2017 2019-07-31T16:42:50Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135725 en 10.1109/CVPRW.2017.286 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Martinez, Daniel Lopez Rudovic, Ognjen Picard, Rosalind W. Personalized Automatic Estimation of Self-Reported Pain Intensity from Facial Expressions |
| title | Personalized Automatic Estimation of Self-Reported Pain Intensity from Facial Expressions |
| title_full | Personalized Automatic Estimation of Self-Reported Pain Intensity from Facial Expressions |
| title_fullStr | Personalized Automatic Estimation of Self-Reported Pain Intensity from Facial Expressions |
| title_full_unstemmed | Personalized Automatic Estimation of Self-Reported Pain Intensity from Facial Expressions |
| title_short | Personalized Automatic Estimation of Self-Reported Pain Intensity from Facial Expressions |
| title_sort | personalized automatic estimation of self reported pain intensity from facial expressions |
| url | https://hdl.handle.net/1721.1/135725 |
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