A machine learning approach to k-step look-ahead prediction of gait variables from acceleration data
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Lai, Daniel 
ORCID: https://orcid.org/0000-0003-3459-7709, Shilton, Alistair, Charry, Edgar, Begg, Rezaul ORCID: https://orcid.org/0000-0002-3195-8591 and Palaniswami, Marimuthu Swami ORCID: https://orcid.org/0000-0002-3635-4252
(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) |
| URI | https://vuir.vu.edu.au/id/eprint/31052 |
| DOI | 10.1109/IEMBS.2009.5334512 |
| Official URL | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arn... |
| ISBN | 9781424432967 |
| Subjects | Historical > FOR Classification > 0903 Biomedical Engineering Historical > FOR Classification > 1106 Human Movement and Sports Science Historical > SEO Classification > 970111 Expanding Knowledge in the Medical and Health Sciences Historical > Faculty/School/Research Centre/Department > Centre for Ageing, Rehabilitation, Exercise & Sport Science (CARES) Current > Division/Research > College of Sports and Exercise Science |
| Keywords | sensitive gait parameter; GRNN; SVR; minimum toe clearance; generalised regression neural network; support vector regressor |
| Citations in Scopus | 8 - View on Scopus |
| Download/View statistics | View download statistics for this item |
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