Multi-task neural networks for personalized pain recognition from physiological signals
Pain is a complex and subjective experience that poses a number of measurement challenges. While self-report by the patient is viewed as the gold standard of pain assessment, this approach fails when patients cannot verbally communicate pain intensity or lack normal mental abilities. Here, we presen...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2019
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| Online Access: | https://hdl.handle.net/1721.1/121967 |
| _version_ | 1826199873992523776 |
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| author | Lopez Martinez, Daniel Picard, Rosalind W. |
| author2 | Massachusetts Institute of Technology. Institute for Medical Engineering & Science |
| author_facet | Massachusetts Institute of Technology. Institute for Medical Engineering & Science Lopez Martinez, Daniel Picard, Rosalind W. |
| author_sort | Lopez Martinez, Daniel |
| collection | MIT |
| description | Pain is a complex and subjective experience that poses a number of measurement challenges. While self-report by the patient is viewed as the gold standard of pain assessment, this approach fails when patients cannot verbally communicate pain intensity or lack normal mental abilities. Here, we present a pain intensity measurement method based on physiological signals. Specifically, we implement a multi-task learning approach based on neural networks that accounts for individual differences in pain responses while still leveraging data from across the population. We test our method in a dataset containing multi-modal physiological responses to nociceptive pain. |
| first_indexed | 2024-09-23T11:27:28Z |
| format | Article |
| id | mit-1721.1/121967 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:27:28Z |
| publishDate | 2019 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1219672022-09-27T19:40:15Z Multi-task neural networks for personalized pain recognition from physiological signals Lopez Martinez, Daniel Picard, Rosalind W. Massachusetts Institute of Technology. Institute for Medical Engineering & Science Harvard University--MIT Division of Health Sciences and Technology Massachusetts Institute of Technology. Media Laboratory Pain is a complex and subjective experience that poses a number of measurement challenges. While self-report by the patient is viewed as the gold standard of pain assessment, this approach fails when patients cannot verbally communicate pain intensity or lack normal mental abilities. Here, we present a pain intensity measurement method based on physiological signals. Specifically, we implement a multi-task learning approach based on neural networks that accounts for individual differences in pain responses while still leveraging data from across the population. We test our method in a dataset containing multi-modal physiological responses to nociceptive pain. 2019-08-02T19:37:08Z 2019-08-02T19:37:08Z 2018-02 2017-10 2019-08-02T11:19:28Z Article http://purl.org/eprint/type/ConferencePaper 978-1-5386-0680-3 https://hdl.handle.net/1721.1/121967 Lopez-Martinez, Daniel and Rosalind Picard. "Multi-task neural networks for personalized pain recognition from physiological signals." Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), October 2017, San Antonio, Texas, USA, IEEE, Febraury 2018 © 2017 IEEE en http://dx.doi.org/10.1109/ACIIW.2017.8272611 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) 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 | Lopez Martinez, Daniel Picard, Rosalind W. Multi-task neural networks for personalized pain recognition from physiological signals |
| title | Multi-task neural networks for personalized pain recognition from physiological signals |
| title_full | Multi-task neural networks for personalized pain recognition from physiological signals |
| title_fullStr | Multi-task neural networks for personalized pain recognition from physiological signals |
| title_full_unstemmed | Multi-task neural networks for personalized pain recognition from physiological signals |
| title_short | Multi-task neural networks for personalized pain recognition from physiological signals |
| title_sort | multi task neural networks for personalized pain recognition from physiological signals |
| url | https://hdl.handle.net/1721.1/121967 |
| work_keys_str_mv | AT lopezmartinezdaniel multitaskneuralnetworksforpersonalizedpainrecognitionfromphysiologicalsignals AT picardrosalindw multitaskneuralnetworksforpersonalizedpainrecognitionfromphysiologicalsignals |