Automated Assessment of Pain (AAP) and Multimodal Sensing Grand Challenge for Next-Gen Pain Assessment (AI4Pain)
Pain communication varies, with some individuals being highly expressive regarding their pain and others exhibiting stoic forbearance and minimal verbal account of discomfort. Considerable progress has been made in defining behavioral indices of pain [1-3]. An abundant literature shows that a limited subset of facial movements, in several non-human species, encode pain intensity across the lifespan [2]. To advance reliable pain monitoring, automated assessment of pain is emerging as a powerful mean to realize that goal. Though progress has been made, this field remains in its infancy. The workshop aims to promote current research and support growth of interdisciplinary collaborations to advance this groundbreaking research.
Authors
- Ben Seymour (ORCID: https://orcid.org/0000-0003-1724-5832)
- Raul Fernandez Rojas (ORCID: https://orcid.org/0000-0002-8393-4241)
- Roland Goecke (ORCID: https://orcid.org/0000-0003-2279-7041)
- Zakia Hammal (ORCID: https://orcid.org/0000-0003-3688-287X)
- Nadia Bianchi‐Berthouze (ORCID: https://orcid.org/0000-0001-8921-0044)
- Steffen Walter (ORCID: https://orcid.org/0000-0001-7165-3541)
Institutions
- University of Canberra (AU)
- UNSW Sydney (AU)
- University of Oxford (GB)
- University Hospital Ulm (DE)
- University College London (GB)
- Carnegie Mellon University (US)
Publication Details
- Published
- 2024-09-15
- DOI
- https://doi.org/10.1109/aciiw63320.2024.00005
- Citations
- 2
- Primary Topic
- Musculoskeletal pain and rehabilitation
- Type
- article
- Field-Weighted Citation Impact
- 0.90
Funders
- National Institutes of Health
- National Institute of Nursing Research