Accessibility navigation


Quantification of advanced Dementia patients’ engagement in therapeutic sessions: an automatic video based approach using computer vision and machine learning

Zhang, L., Arandjelovic, O., Dewar, S., Astell, A., Doherty, G. and Ellisgie, M. (2020) Quantification of advanced Dementia patients’ engagement in therapeutic sessions: an automatic video based approach using computer vision and machine learning. In: International Conference of the IEEE Engineering in Medicine and Biology Society, 20-24 July 2020, Montreal, Canada. (In Press)

[img] Text - Accepted Version
· Restricted to Repository staff only
· The Copyright of this document has not been checked yet. This may affect its availability.

1MB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Abstract/Summary

Most individuals with advanced dementia lose the ability to communicate with the outside world through speech. This limits their ability to participate in social activities crucial to their well-being and quality of life. However, there is mounting evidence that individuals with advanced dementia can still communicate non-verbally and benefit greatly from these interactions. A major problem in facilitating the advancement of this research is of a practical and methodical nature: assessing the success of treatment is currently done by humans, prone to subjective bias and inconsistency, and it involves laborious and time consuming effort. The present work is the first attempt at exploring if automatic (artificial intelligence based) quantification of the degree of patient engagement in Adaptive Interaction sessions, a highly promising intervention developed to improve the quality of life of nonverbal individuals with advanced dementia. Hence we describe a framework which uses computer vision and machine learning as a potential first step towards answering this question. Using a real-world data set of videos of therapeutic sessions, not acquired specifically for the purposes of the present work, we demonstrate highly promising results.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Psychology and Clinical Language Sciences > Ageing
Faculty of Life Sciences > School of Psychology and Clinical Language Sciences > Department of Psychology
ID Code:90669

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation