Abnormal Infant Movements Classification with Deep Learning on Pose-based Features
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 27498182
- Type
- D - Journal article
- DOI
-
10.1109/ACCESS.2020.2980269
- Title of journal
- IEEE Access
- Article number
- 9034058
- First page
- 51582
- Volume
- 8
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2020
- 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
-
5
- Research group(s)
-
F - Cyber Security and Network Systems (CyberNets)
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper supports new approaches to the early diagnosis of movement disorders, such as cerebral palsy, by evaluating the viability of automatically analysing pose and joint specific movements as a means of diagnosing movement conditions. Dr Ho and the team have successfully obtained the Research Ethics Committee and Health Research Authority Approval (ref: IRAS 252317) to test the proposed method on data captured from NHS patients. The annotated data and the implementation of the proposed framework, which are currently rarely available in the public domain, is published online (URL: https://github.com/edmondslho/SMARTBabies) to stimulate research in this area.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
