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.
Documents
23594:551197
PDF
Sherkat_ClinicalAssessmentDepth(VoR).pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Sherkat_ClinicalAssessmentDepth(VoR).pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Download (1MB) | Preview
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.
More Information
Statistics
Downloads
Downloads per month over past year
Metrics
Altmetric Badge
Dimensions Badge
Share
Actions (login required)
![]() |
View Item |