EmoPain@Home dataset

The EmoPain@Home dataset contains body movement data from people with and without chronic pain captured during everyday physical activities in their homes. People wearing a set of wearable movement sensors in the upper and lower body, was engaging in everyday tasks insider their home, task that they found enjoyable or demanding. The dataset includes labels for pain, worry, and confidence levels for participants with chronic pain. It was developed as part of the H2020 EU EnTimeMent project (uclic.ucl.ac.uk/research/affective-computing/entimement).

To know more about the dataset, see:

Olugbade, T., Buono, R., Williams, A., De Ossorno Garcia, S., Gold, N., Holloway, C., Berthouze, N. (2022). EmoPain(at)Home: Dataset and Automatic Assessment within Functional Activity for Chronic Pain Rehabilitation. Proceedings of the 2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII'22), IEEE

T. Olugbade, A. C. d. C. Williams, N. Gold and N. Bianchi-Berthouze. Movement Representation Learning for Pain Level Classification. In IEEE Transactions on Affective Computing, vol. , no. 01, pp. 1-12, 5555. doi: 10.1109/TAFFC.2023.3334522.

To access the dataset, please email  Professor Nadia Berthouze, with 'Dataset request' in the subject.

We look forward to sharing our dataset!