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Proceedings Paper

Signal analysis of accelerometry data using gravity-based modeling
Author(s): Neil P. Davey; Daniel A. James; Megan E Anderson
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Paper Abstract

Triaxial accelerometers have been used to measure human movement parameters in swimming. Interpretation of data is difficult due to interference sources including interaction of external bodies. In this investigation the authors developed a model to simulate the physical movement of the lower back. Theoretical accelerometery outputs were derived thus giving an ideal, or noiseless dataset. An experimental data collection apparatus was developed by adapting a system to the aquatic environment for investigation of swimming. Model data was compared against recorded data and showed strong correlation. Comparison of recorded and modeled data can be used to identify changes in body movement, this is especially useful when cyclic patterns are present in the activity. Strong correlations between data sets allowed development of signal processing algorithms for swimming stroke analysis using first the pure noiseless data set which were then applied to performance data. Video analysis was also used to validate study results and has shown potential to provide acceptable results.

Paper Details

Date Published: 30 March 2004
PDF: 9 pages
Proc. SPIE 5274, Microelectronics: Design, Technology, and Packaging, (30 March 2004); doi: 10.1117/12.530184
Show Author Affiliations
Neil P. Davey, Griffith Univ. (Australia)
Daniel A. James, Griffith Univ. (Australia)
Megan E Anderson, Australian Institute of Sport (Australia)

Published in SPIE Proceedings Vol. 5274:
Microelectronics: Design, Technology, and Packaging
Derek Abbott; Kamran Eshraghian; Charles A. Musca; Dimitris Pavlidis; Neil Weste, Editor(s)

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