Wavelet-frequency analysis for the detection of discontinuities in switched system models of human balance |
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| Institution: | 1. School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, UK;2. School of Healthcare Science, Manchester Metropolitan University, Manchester, UK;1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada;2. Department of Oncology, University of Alberta, Edmonton, Alberta, Canada;1. School of Health and Rehabilitation Sciences, University of Queensland, St Lucia, QLD 4072, Australia;2. School of Exercise Science, Australian Catholic University, Fitzroy, Victoria 3065, Australia;3. ‘Department of Physiotherapy, Singapore General Hospital,, Outram Road, Singapore 169608, Singapore;1. Balance and Gait Laboratory, Department of Kinesiology, Brock University, St. Catharines, ON, Canada;2. Neural Control of Posture and Movement Laboratory, School of Kinesiology, University of British Columbia, Vancouver, BC, Canada |
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| Abstract: | This paper is concerned with detecting the presence of switching behavior in experimentally obtained posturographic data sets by means of a novel algorithm that is based on a combination of wavelet analysis and Hilbert transform. As a test-bed for the algorithm, we first use a switched model of human balance control during quiet standing with known switching behavior in four distinct configurations. We obtain a time–frequency representation of a signal generated by our model system. We are then able to detect manifestations of discontinuities (switchings) in the signal as spiking behavior. The frequency of switchings, measured by means of our algorithm and detected in our models systems, agrees with the frequency of spiking behavior found in the experimentally obtained posturographic data. |
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| Keywords: | Switched systems Wavelets Normalized Hilbert transform Discontinuities Instantaneous frequency |
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