Authors:
Chetanya Puri
1
;
Stijn Keyaerts
2
;
3
;
Maxwell Szymanski
4
;
5
;
Lode Godderis
2
;
3
;
Katrien Verbert
4
;
Stijn Luca
6
and
Bart Vanrumste
1
Affiliations:
1
eMedia Lab and STADIUS, Department of Electrical Engineering (ESAT), KU Leuven, Belgium
;
2
Knowledge, Information and Research Center (KIR), Group Idewe (External Service for Prevention and Protection at Work), Leuven, Belgium
;
3
Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
;
4
Department of Computer Science, KU Leuven, Belgium
;
5
Human-computer Interaction research group, Department of Computer Science, KU Leuven, Belgium
;
6
Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
Keyword(s):
Pain Management, Public Health Informatics, Time Series Forecasting, Bayesian Prediction.
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 subj
ect 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.
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