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

SARSFIELD, J, BROWN, D, SHERKAT, N, LANGENSIEPEN, C, LEWIS, J, TAHERI, M, MCCOLLIN, C, BARNETT, C, SELWOOD, L, STANDEN, P, LOGAN, P, SIMCOX, C, KILLICK, C and 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.

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Abstract

© 2018 The Authors 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.

Item Type:	Article
Uncontrolled Keywords:	08 Information And Computing Sciences; 09 Engineering; 11 Medical And Health Sciences; Medical Informatics
Identification Number:	https://doi.org/10.1016/j.ijmedinf.2018.11.001
Page Range:	30-38
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	10 Jan 2019 11:37
Last Modified:	29 Apr 2021 14:22
URI:	https://shura.shu.ac.uk/id/eprint/23594

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