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A machine learning approach to k-step look-ahead prediction of gait variables from acceleration data

Lai, Tze huei ORCID: 0000-0003-3459-7709, Shilton, A, Charry, E, Begg, Rezaul ORCID: 0000-0002-3195-8591 and Palaniswami, M (2009) A machine learning approach to k-step look-ahead prediction of gait variables from acceleration data. In: 31st Annual International Conference of the IEEE - Engineering in Medicine and Biology Society (EMBS), 02 September 2009-06 September 2009, Minneapolis, Minnesota.

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Item Type: Conference or Workshop Item (Paper)
ISBN: 9781424432967
Uncontrolled Keywords: sensitive gait parameter; GRNN; SVR; minimum toe clearance; generalised regression neural network; support vector regressor
Subjects: FOR Classification > 0903 Biomedical Engineering
FOR Classification > 1106 Human Movement and Sports Science
SEO Classification > 970111 Expanding Knowledge in the Medical and Health Sciences
Faculty/School/Research Centre/Department > Centre for Ageing, Rehabilitation, Exercise & Sport Science (CARES)
Faculty/School/Research Centre/Department > College of Sports and Exercise Science
Depositing User: Symplectic Elements
Date Deposited: 07 Jul 2016 23:21
Last Modified: 19 Apr 2018 12:50
URI: http://vuir.vu.edu.au/id/eprint/31052
DOI: https://doi.org/10.1109/IEMBS.2009.5334512
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Citations in Scopus: 7 - View on Scopus

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