Clinical assessment of depth sensor based pose estimation algorithms for technology supervised rehabilitation applications

Sarsfield, Joe; Brown, David; Sherkat, Nasser; Langensiepen, Caroline; Lewis, James; Taheri, Mohammad; McCollin, Christopher; Barnett, Cleveland; Selwood, Louise; Standen, Penny; Logan, Pip; Simcox, Christopher; Killick, Catherine; Hughes, Emma

Abstract

Encouraging rehabilitation by the use of technology in the home can be a cost-effective strategy, particularly if consumer-level equipment can be used. We present a clinical qualitative and quantitative analysis of the pose estimation algorithms of a typical consumer unit (Xbox One Kinect), to assess its suitability for technology supervised rehabilitation and guide development of future pose estimation algorithms for rehabilitation applications. We focused the analysis on upper-body stroke rehabilitation as a challenging use case. We found that the algorithms require improved joint tracking, especially for the shoulder, elbow and wrist joints, and exploiting temporal information for tracking when there is full or partial occlusion in the depth data.

Citation

Sarsfield, J., Brown, D., Sherkat, N., Langensiepen, C., Lewis, J., Taheri, M., …Hughes, E. (2019). Clinical assessment of depth sensor based pose estimation algorithms for technology supervised rehabilitation applications. International Journal of Medical Informatics, 121, 30-38. https://doi.org/10.1016/j.ijmedinf.2018.11.001

• Journal Article Type: Article
• Acceptance Date: Nov 5, 2018
• Online Publication Date: Nov 8, 2018
• Publication Date: 2019-01
• Deposit Date: Apr 30, 2019
• Publicly Available Date: May 1, 2019
• Journal: International Journal of Medical Informatics
• Print ISSN: 1386-5056
• Electronic ISSN: 1872-8243
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 121
• Pages: 30-38
• DOI: https://doi.org/10.1016/j.ijmedinf.2018.11.001
• Keywords: Clinical evaluation; Depth sensors; Home rehabilitation; Pose estimation accuracy; Stroke rehabilitation
• Public URL: https://nottingham-repository.worktribe.com/output/1883757
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1386505618312759?via%3Dihub
• Additional Information: This article is maintained by: Elsevier; Article Title: Clinical assessment of depth sensor based pose estimation algorithms for technology supervised rehabilitation applications; Journal Title: International Journal of Medical Informatics; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.ijmedinf.2018.11.001; Content Type: article; Copyright: © 2018 The Authors. Published by Elsevier B.V.