A wavelet packet based approach for predicting voluntary joint flexion force from VMG. Jason Paul Cole

ISBN: 9780549582731

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96 pages

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A wavelet packet based approach for predicting voluntary joint flexion force from VMG. by Jason Paul Cole

A wavelet packet based approach for predicting voluntary joint flexion force from VMG. by Jason Paul Cole
| NOOKstudy eTextbook | PDF, EPUB, FB2, DjVu, talking book, mp3, ZIP | 96 pages | ISBN: 9780549582731 | 8.54 Mb

This thesis examines the association between isolated VMG components and voluntary static forearm flexion force (SFFF) against dynamic constant external resistance (DCER) obtained from the brachioradialis of healthy adult men and women duringMoreThis thesis examines the association between isolated VMG components and voluntary static forearm flexion force (SFFF) against dynamic constant external resistance (DCER) obtained from the brachioradialis of healthy adult men and women during contractions ranging in force from zero to maximum sustainable load.

The VMG recordings were decomposed using wavelet packet analysis, and crystal amplitude estimates were utilized in bi-variate regression models to identify VMG components whose amplitudes best correlated to SFFF. Findings indicate that one crystal was sufficient to accurately predict SFFF in both men (R2 = 0.970, P < 0.0001) and women (R 2 = 0.978, P < 0.0001). Moreover, the best correlations were observed against absolute force, suggesting that VMG recordings are capable of providing a objective and non-invasive measure of joint flexion force using data obtained from a non-pennate muscle during static DCER, and so may have applications in both sports medicine and rehabilitation.



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