Show simple item record

dc.contributor.authorSimcox, Christopher
dc.contributor.authorKillick, Catherine
dc.contributor.authorHughes, Emma
dc.date.accessioned2018-12-21T09:53:12Z
dc.date.available2018-12-21T09:53:12Z
dc.date.issued2019
dc.identifier.citationSarsfield, J., Brown, D., Sherkat, N., Langensiepen, C., Lewis, J., Taheri, M., McCollin, C., Barnett, C., Selwood, L., Standen, P., et al. (2019). Clinical assessment of depth sensor based pose estimation algorithms for technology supervised rehabilitation applications. International Journal of Medical Informatics, 121 (January), pp.30-38.en
dc.identifier.other10.1016/j.ijmedinf.2018.11.001
dc.identifier.urihttp://hdl.handle.net/20.500.12904/11770
dc.description.abstractEncouraging 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. Copyright © 2018 The Authors
dc.description.urihttps://www.sciencedirect.com/science/article/pii/S1386505618312759?via%3Dihuben
dc.subjectStroke rehabilitationen
dc.subjectRehabilitationen
dc.subjectTelemedicineen
dc.titleClinical assessment of depth sensor based pose estimation algorithms for technology supervised rehabilitation applicationsen
dc.typeArticleen
html.description.abstractEncouraging 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.<br/>Copyright &#xa9; 2018 The Authors


This item appears in the following Collection(s)

Show simple item record