Olugbade, Temitayo;
Buono, Raffaele Andrea;
Potapov, Kyrill;
Bujorianu, Alex;
Williams, Amanda C de C;
De Ossorno Garcia, Santiago;
Gold, Nicolas;
... Bianchi-Berthouze, Nadia; + view all
(2024)
The EmoPain@Home Dataset: Capturing Pain Level and Activity Recognition for People with Chronic Pain in Their Homes.
IEEE Transactions on Affective Computing
pp. 1-14.
10.1109/TAFFC.2024.3390837.
(In press).
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Abstract
Chronic pain is a prevalent condition where fear of movement and pain interfere with everyday functioning. Yet, there is no open body movement dataset for people with chronic pain in everyday settings. Our EmoPain@Home dataset addresses this with capture from 18 people with and without chronic pain in their homes, while they performed their routine activities. The data includes labels for pain, worry, and movement confidence continuously recorded for activity instances for the people with chronic pain. We explored baseline two-level pain detection based on this dataset and obtained 0.62 mean F1 score. However, extension of the dataset led to deterioration in performance confirming high variability in pain expressions for real world settings. We investigated baseline activity recognition for this setting as a first step in exploring the use of the activity label as contextual information for improving pain level classification performance. We obtained mean F1 score of 0.43 for 9 activity types, highlighting its feasibility. Further exploration, however, showed that data from healthy people cannot be easily leveraged for improving performance because worry and low confidence alter activity strategies for people with chronic pain. Our dataset and findings lay critical groundwork for automatic assessment of pain experience and behaviour in the wild.
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