Kinect posture reconstruction based on a local mixture of Gaussian process models
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 22062347
- Type
- D - Journal article
- DOI
-
10.1109/TVCG.2015.2510000
- Title of journal
- IEEE Transactions on Visualization and Computer Graphics
- Article number
- -
- First page
- 2437
- Volume
- 22
- Issue
- 11
- ISSN
- 1077-2626
- Open access status
- Out of scope for open access requirements
- Month of publication
- December
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
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D - Computer Vision and Natural Computing (CVNC)
- Citation count
- 22
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The potential in healthcare applications of this idea enabling Microsoft Kinect to accurately obtain 3D self-occluded human posture resulted in an invited seminar in the Innovation Showcase Digital by NHS City Hospitals Sunderland (Imran.Ahmed@chsft.nhs.uk). Its applications in human-aware smart environment were the basis of a subsequent Royal Society project (“An Affective Smart Environment for Personalized Learning and Teaching”, IE160609) with Dr. Chattopadhyay (Jadavpur University, India, matanginic@gmail.com) and led to an invited talk at Hokkaido University Japan (“Human Motion Understanding”, invited by doba@ime.ist.hokudai.ac.jp). The research itself was funded by EPSRC (EP/M002632/1) from the healthcare (Nine Health) and sports sectors (Cadence, Kinesio).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
