Automatic Musculoskeletal and Neurological Disorder Diagnosis with Relative Joint Displacement from Human Gait
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 25206429
- Type
- D - Journal article
- DOI
-
10.1109/TNSRE.2018.2880871
- Title of journal
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Article number
- -
- First page
- 2387
- Volume
- 26
- Issue
- 12
- ISSN
- 1534-4320
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2018
- 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
- Yes
- Number of additional authors
-
4
- Research group(s)
-
D - Computer Vision and Natural Computing (CVNC)
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Ageing Society is one of the Grand Challenges in the UK Industrial Strategy. Here, the key advance of diagnosing musculoskeletal and neurological disorders from older people was the basis of a subsequent Royal Society project (“Autonomous_Monitoring_for_Patients_and_Older_People_using_Smart_Environments_with_Sensor_Fusion”, IES\R1\191147) with Prof. Samiran Chattopadhyay (Jadavpur University, India, samirancju@gmail.com) and led to a collaboration with Sunderland City Council (SCC) on a pilot study in monitoring older people behaviours in a smart home environment (Dave Young Dave.Young@sunderland.gov.uk). Motion data from the paper with anonymised medical history of the subjects has been provided online as a community resource (URL: http://hubertshum.com/info/publications/tnsre2018/files/database.zip).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
