Date published: 03/10/18
The use of multiview videos in measuring depression
- Name: Bruhanth Mallik
- Current Organisation: Leeds Beckett University
In May, Translate opened its summer student project scheme to support small medical technology development projects in the Leeds City Region. The scheme proved to be a massive success and 26 unique projects were funded. Learn more about their work in this blog.
My name is Bruhanth Mallik and I am finishing a PhD on 3D videos under Dr Akbar Sheikh Akbari’s supervision at School of Computing, Creative Technology and Engineering, Leeds Beckett University. My PhD research is primarily focused towards developing 3D video processing techniques. The class of 3D videos I am looking into are texture based multiview videos. In addition to multimedia applications, multiview videos have found a range of applications in the medical field.
I am very keen to broaden my knowledge of 3D video applications, as this is an actively studied field in computer vision. I am strongly drawn towards the application of multiview videos in the field of medicine, especially for the study of depression due to its non-invasive nature of diagnosis. The summer student project has been given me an excellent opportunity to work on such a subject, and has allowed me to build my skills in multiview videos applications and analysis.
The aim of the project is to develop a proof of concept of an autonomous system that can read changes in facial features and quantify the severity of depression in adults. This would complement questionnaire based psychometric diagnosis results. In this project my role is to develop a multiview videos based algorithm to accurately analyze and precisely measure the subtle motion changes in facial muscles, which would assist in identifying the intensity of a patient’s depression condition. The multiview videos based algorithm will then be used to develop a test model, which would then be used to conduct a feasibility study. Ultimately, this research could result in developing a non-invasive technology for helping doctors to measure the level of depression in patients.
