Daily Pain Prediction in Workplace Using Gaussian Processes

Chetanya Puri, Stijn Keyaerts, Stijn Keyaerts, Maxwell Szymanski, Maxwell Szymanski, Lode Godderis, Lode Godderis, Katrien Verbert, Stijn Luca, Bart Vanrumste

2023

Abstract

Work-related Musculoskeletal disorders (MSDs) account for 60% of sickness-related absences and even permanent inability to work in the Europe. Long term impacts of MSDs include “Pain chronification” which is the transition of temporary pain into persistent pain. Preventive pain management can lower the risk of chronic pain. It is therefore important to appropriately assess pain in advance, which can assist a person in improving their fear of returning to work. In this study, we analysed pain data acquired over time by a smartphone application from a number of participants. We attempt to forecast a person’s future pain levels based on his or her prior pain data. Due to the self-reported nature of the data, modelling daily pain is challenging due to the large number of missing values. For pain prediction modelling of a test subject, we employ a subset selection strategy that dynamically selects a closest subset of individuals from the training data. The similarity between the test subject and the training subjects is determined via dynamic time warping-based dissimilarity measure based on the time limited historical data until a given point in time. The pain trends of these selected subset subjects is more similar to that of the individual of interest. Then, we employ a Gaussian processes regression model for modelling the pain. We empirically test our model using a leave-one-subject-out cross validation to attain 20% improvement over state-of-the-art results in early prediction of pain.

Paper Citation

in Harvard Style

Puri C., Keyaerts S., Szymanski M., Godderis L., Verbert K., Luca S. and Vanrumste B. (2023). Daily Pain Prediction in Workplace Using Gaussian Processes. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF; ISBN 978-989-758-631-6, SciTePress, pages 239-247. DOI: 10.5220/0011611200003414

in Bibtex Style

@conference{healthinf23,
author={Chetanya Puri and Stijn Keyaerts and Maxwell Szymanski and Lode Godderis and Katrien Verbert and Stijn Luca and Bart Vanrumste},
title={Daily Pain Prediction in Workplace Using Gaussian Processes},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF},
year={2023},
pages={239-247},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011611200003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF
TI - Daily Pain Prediction in Workplace Using Gaussian Processes
SN - 978-989-758-631-6
AU - Puri C.
AU - Keyaerts S.
AU - Szymanski M.
AU - Godderis L.
AU - Verbert K.
AU - Luca S.
AU - Vanrumste B.
PY - 2023
SP - 239
EP - 247
DO - 10.5220/0011611200003414
PB - SciTePress