Development of a Pain Signaling System Using Machine Learning

Helen Korving; Sheng Li; Di Zhou; Paula Sterkenburg; Panos Markopoulos; Emilia Barakova

doi:10.1109/ICASSPW59220.2023.10193643

Development of a Pain Signaling System Using Machine Learning

Helen Korving, Sheng Li, Di Zhou, Paula Sterkenburg, Panos Markopoulos, Emilia Barakova

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

Abstract

A wearable pain signaling system is introduced, to be used in the daily care for people with severe and profound intellectual disabilities. Due to several medical disorders, this group of people can experience daily acute pain and chronic pain, which they have trouble expressing and communicating to caregivers. The system consists of a smart sock with fabric sensors, a sensor unit sending a 6A current through the smart sock receiving electrodermal response and a mobile application containing a machine learning algorithm translating the signal. The pain signaling algorithm uses data of one to five painful stimuli from 28 healthy participants. Random forest modeling was used to classify moments of pain and train a model to predict pain from new data. The algorithm's accuracy could be improved by an ensemble of five models and voting, so this groundbreaking system can become a much-wanted attribution to daily caregiving of people with disabilities.

Original language	English
Title of host publication	ICASSPW 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing Workshops, Proceedings
Publisher	Institute of Electrical and Electronics Engineers
ISBN (Electronic)	9798350302615
DOIs	https://doi.org/10.1109/ICASSPW59220.2023.10193643
Publication status	Published - 2023
Event	48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece Duration: 4 Jun 2023 → 10 Jun 2023

Conference

Conference	48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Abbreviated title	ICASSP 2023
Country/Territory	Greece
City	Rhodes Island
Period	4/06/23 → 10/06/23

Bibliographical note

Funding Information:
Funding for this project was granted by the Netherlands Organisation for Health Research and Development, ZonMw, Den Haag, The Netherlands, under grant number 8084 5009 8345. This project and all its components were approved by the medical-ethical committee of the Vrije Universiteit Medical Centre (METc VUmc) in April 2020; NL69815.029.19.

Publisher Copyright:
© 2023 IEEE.

Keywords

algorithm development
electrodermal activity
Pain signaling
smart sock
wearable sensor

Access to Document

10.1109/ICASSPW59220.2023.10193643

Cite this

Korving, H., Li, S., Zhou, D., Sterkenburg, P., Markopoulos, P., & Barakova, E. (2023). Development of a Pain Signaling System Using Machine Learning. In ICASSPW 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing Workshops, Proceedings Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICASSPW59220.2023.10193643

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title = "Development of a Pain Signaling System Using Machine Learning",

abstract = "A wearable pain signaling system is introduced, to be used in the daily care for people with severe and profound intellectual disabilities. Due to several medical disorders, this group of people can experience daily acute pain and chronic pain, which they have trouble expressing and communicating to caregivers. The system consists of a smart sock with fabric sensors, a sensor unit sending a 6A current through the smart sock receiving electrodermal response and a mobile application containing a machine learning algorithm translating the signal. The pain signaling algorithm uses data of one to five painful stimuli from 28 healthy participants. Random forest modeling was used to classify moments of pain and train a model to predict pain from new data. The algorithm's accuracy could be improved by an ensemble of five models and voting, so this groundbreaking system can become a much-wanted attribution to daily caregiving of people with disabilities.",

keywords = "algorithm development, electrodermal activity, Pain signaling, smart sock, wearable sensor",

author = "Helen Korving and Sheng Li and Di Zhou and Paula Sterkenburg and Panos Markopoulos and Emilia Barakova",

note = "Funding Information: Funding for this project was granted by the Netherlands Organisation for Health Research and Development, ZonMw, Den Haag, The Netherlands, under grant number 8084 5009 8345. This project and all its components were approved by the medical-ethical committee of the Vrije Universiteit Medical Centre (METc VUmc) in April 2020; NL69815.029.19. Publisher Copyright: {\textcopyright} 2023 IEEE.; 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",

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doi = "10.1109/ICASSPW59220.2023.10193643",

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Korving, H, Li, S, Zhou, D, Sterkenburg, P, Markopoulos, P & Barakova, E 2023, Development of a Pain Signaling System Using Machine Learning. in ICASSPW 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing Workshops, Proceedings. Institute of Electrical and Electronics Engineers, 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023, Rhodes Island, Greece, 4/06/23. https://doi.org/10.1109/ICASSPW59220.2023.10193643

Development of a Pain Signaling System Using Machine Learning. / Korving, Helen; Li, Sheng; Zhou, Di et al.
ICASSPW 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing Workshops, Proceedings. Institute of Electrical and Electronics Engineers, 2023.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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N1 - Funding Information: Funding for this project was granted by the Netherlands Organisation for Health Research and Development, ZonMw, Den Haag, The Netherlands, under grant number 8084 5009 8345. This project and all its components were approved by the medical-ethical committee of the Vrije Universiteit Medical Centre (METc VUmc) in April 2020; NL69815.029.19. Publisher Copyright: © 2023 IEEE.

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N2 - A wearable pain signaling system is introduced, to be used in the daily care for people with severe and profound intellectual disabilities. Due to several medical disorders, this group of people can experience daily acute pain and chronic pain, which they have trouble expressing and communicating to caregivers. The system consists of a smart sock with fabric sensors, a sensor unit sending a 6A current through the smart sock receiving electrodermal response and a mobile application containing a machine learning algorithm translating the signal. The pain signaling algorithm uses data of one to five painful stimuli from 28 healthy participants. Random forest modeling was used to classify moments of pain and train a model to predict pain from new data. The algorithm's accuracy could be improved by an ensemble of five models and voting, so this groundbreaking system can become a much-wanted attribution to daily caregiving of people with disabilities.

AB - A wearable pain signaling system is introduced, to be used in the daily care for people with severe and profound intellectual disabilities. Due to several medical disorders, this group of people can experience daily acute pain and chronic pain, which they have trouble expressing and communicating to caregivers. The system consists of a smart sock with fabric sensors, a sensor unit sending a 6A current through the smart sock receiving electrodermal response and a mobile application containing a machine learning algorithm translating the signal. The pain signaling algorithm uses data of one to five painful stimuli from 28 healthy participants. Random forest modeling was used to classify moments of pain and train a model to predict pain from new data. The algorithm's accuracy could be improved by an ensemble of five models and voting, so this groundbreaking system can become a much-wanted attribution to daily caregiving of people with disabilities.

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ER -

Development of a Pain Signaling System Using Machine Learning

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Keywords

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