Abstract
Recent research work indicates that gait patterns are both non-linear and non-stationary signals and they can be analyzed using empirical mode decomposition. This paper describes gait pattern classification using features that are obtained by performing discrete cosine transforms (DCT) on intrinsic mode functions of five different human gait patterns. The DCT provides a compact 8-dimensional feature vector for gait pattern classification. Fifty two subjects participated in the experiment. The classification was performed using a Gaussian mixture model and an overall accuracy of 90.2% was achieved.
Original language | English |
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Title of host publication | Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 |
Place of Publication | Piscataway, NJ |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 3852-3855 |
Number of pages | 4 |
ISBN (Print) | 9781424418152 |
DOIs | |
Publication status | Published - 2008 |
Event | 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - Vancouver, BC, Canada Duration: 20 Aug 2008 → 25 Aug 2008 |
Other
Other | 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 |
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Country/Territory | Canada |
City | Vancouver, BC |
Period | 20/08/08 → 25/08/08 |