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Pain fingerprinting using multimodal sensing : pilot study |
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| Author: | Keskinarkaus, Anja1; Yang, Ruijing1,2; Fylakis, Angelos1; |
| Organizations: |
1Center for Machine Vision and Signal Analysis (CMVS), Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland 2School of Information Science and Technology, Northwest University, Xi’an, China 3Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
4Neural Eng. & Clin Electrophysiology Laboratory, Department of Orthopaedics & Traumatology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Pok Fu Lam, Hong Kong
5Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland |
| Format: | article |
| Version: | published version |
| Access: | open |
| Online Access: | PDF Full Text (PDF, 2.8 MB) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe2022030922636 |
| Language: | English |
| Published: |
Springer Nature,
2022
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| Publish Date: | 2022-03-09 |
| Description: |
AbstractPain is a complex phenomenon, the experience of which varies widely across individuals. At worst, chronic pain can lead to anxiety and depression. Cost-effective strategies are urgently needed to improve the treatment of pain, and thus we propose a novel home-based pain measurement system for the longitudinal monitoring of pain experience and variation in different patients with chronic low back pain. The autonomous nervous system and audio-visual features are analyzed from heart rate signals, voice characteristics and facial expressions using a unique measurement protocol. Self-reporting is utilized for the follow-up of changes in pain intensity, induced by well-designed physical maneuvers, and for studying the consecutive trends in pain. We describe the study protocol, including hospital measurements and questionnaires and the implementation of the home measurement devices. We also present different methods for analyzing the multimodal data: electroencephalography, audio, video and heart rate. Our intention is to provide new insights using technical methodologies that will be beneficial in the future not only for patients with low back pain but also patients suffering from any chronic pain. see all
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| Series: |
Multimedia tools and applications |
| ISSN: | 1380-7501 |
| ISSN-E: | 1573-7721 |
| ISSN-L: | 1380-7501 |
| Volume: | 81 |
| Issue: | 4 |
| Pages: | 5717 - 5742 |
| DOI: | 10.1007/s11042-021-11761-8 |
| OADOI: | https://oadoi.org/10.1007/s11042-021-11761-8 |
| Type of Publication: |
A1 Journal article – refereed |
| Field of Science: |
113 Computer and information sciences 3121 General medicine, internal medicine and other clinical medicine |
| Subjects: | |
| Funding: |
Open Access funding provided by University of Oulu including Oulu University Hospital. University of Oulu, The National Technology Agency of Finland (Business Finland). |
| Copyright information: |
© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
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https://creativecommons.org/licenses/by/4.0/ |