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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

Authors

Joe Sarsfield

David Brown

Nasser Sherkat

Caroline Langensiepen

James Lewis

Mohammad Taheri

Christopher McCollin

Cleveland Barnett

Louise Selwood

Penny Standen

PIP LOGAN pip.logan@nottingham.ac.uk
Professor of Rehabilitation Research

Christopher Simcox

Catherine Killick

Emma Hughes



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.

Journal Article Type Article
Publication Date 2019-01
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
APA6 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
DOI https://doi.org/10.1016/j.ijmedinf.2018.11.001
Keywords Clinical evaluation; Depth sensors; Home rehabilitation; Pose estimation accuracy; Stroke rehabilitation
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.

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