Many current clinical practices rely on subjective evaluations performed by trained clinicians. These evaluations suffer from low reliability and require long and specialized training to establish an expert level of performance. For many conditions, the diagnosis is delayed and on-going monitoring is limited. The ability to track progression pre and post-intervention is also limited.  There is a clear need across various areas of healthcare, and particularly in neurology and rehabilitation medicine, for objective methods that support clinician-administered and in-home assessments based on readily available technologies and rapidly emerging analytics. The automatic analyses of face and body gestures and movements using computer vision and machine learning techniques offers a unique opportunity to revolutionize the landscape of assessment in neurological disorders. 

While the call is open to all submission related to the topic (face and body movement analysis – applications in healthcare), we especially invite papers resulting from collaboration between technical and clinical experts. Topics of interest include (but are not limited to):

  • Monitoring patients’ health based on face and body movement analysis;
  • Assessing physical and/or cognitive ability based on face and body movement analysis;
  • Orofacial assessment in clinical populations;
  • Hand function assessment in clinical populations;
  • Assessment of gait and/or balance;
  • Assistive technology;
  • Rehabilitation monitoring; and
  • Injury prevention.

Healthcare application of face and gesture analysis is a growing area of research. Truly innovative advances in the field require significant collaboration between technical (computer science, engineering) and clinical (neurology, psychology, speech language pathology, physical therapy, geriatrics, pediatrics, etc.) experts. This FG Special Session represents a venue for fostering these collaborations, providing a unique and welcoming environment for transdisciplinary research that is sometimes labeled as being “too clinical” by technical journals or “too technical” by clinical journals. 


  • Babak Taati, Ph.D. P.Eng.
  • Diego Guarin, Ph.D.
  • Andrea Bandini, Ph.D.
  • Yana Yunusova, Ph.D., CCC-SLP