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

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Lai, Daniel ORCID: 0000-0003-3459-7709, Shilton, Alistair, Charry, Edgar, Begg, Rezaul ORCID: 0000-0002-3195-8591 and Palaniswami, Marimuthu Swami ORCID: 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
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