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Zakia Hammal, PhDZamia HammalZamia Hammal

Carnegie Mellon University
The Robotics Institut
Pittsbourgh, PA (USA)

PD Dr. Steffen WalterSteffen Walter, UlmSteffen Walter, Ulm

Ulm University (D)
Medical Psychology

Nadia Berthouze NadiaNadia

University College London (UK)
Affective Interaction and Computing

Automated Assessment for Pain (AAP 2020)

International Workshop

Pain typically is measured by patient self-report, but self-reported pain is difficult to interpret and may be impaired or in some circumstances not possible to obtain. For instance, in patients with restricted verbal abilities such as neonates, young children, and in patients with certain neurological or psychiatric impairments (e.g., dementia). Additionally, the subjectively experienced pain may be partly or even completely unrelated to the somatic pathology of tissue damage and other disorders. Therefore, the standard self-assessment of pain does not always allow for an objective and reliable assessment of the quality and intensity of pain. Given individual differences among patients, their families, and healthcare providers, pain often is poorly assessed, underestimated, and inadequately treated. To improve assessment of pain, objective, valid, and efficient assessment of the onset, intensity, and pattern of occurrence of pain is necessary. To address these needs, several efforts have been made in machine learning and computer vision community for automatic and objective assessment of pain from video as a powerful alternative to self-reported pain.

Publicly Available Pain Databases

UNBC-McMaster Shoulder Pain, BioVid Heat Pain, BP4D-Spontaneous, BP4D+, COPE

Non-publicly Available Pain Databases

EmoPain, SenseEmotion, X-ITE Pain


The workshop aims to bring together interdisciplinary researchers working in field of automatic multimodal assessment of pain (using video and physiological signals). A key focus of the workshop is the translation of laboratory work into clinical practice. Topics of interest include, but are not limited to:

  • Multimodal assessment of pain intensity from video (e.g., face, head, body)
  • Multimodal assessment of pain intensity from physiological signals
  • Clinically relevant chronic and acute pain corpora recording and annotation
    for infants, adults, and adults with restricted communication
  • Applications of technology for pain and related states recognition