We studied the initiation and sequence of digital joint motion during unrestricted flexion and extension using a 3-D motion analysis of all fingers moving simultaneously. Our results showed that motion started in a single joint in 83%, of flexion and 80%, of extension cycles. The DIP joint initiated flexion and extension in the index, middle, and ring fingers, but in the little finger, flexion started in the PIP joint, and extension in the MP joint. The two most frequent sequences of joint movement during flexion of the three radial fingers were DIP-PIP-MP and PIP-DIP-MP. The two most frequent sequences during extension of the three radial fingers were DIP-MP-PIP followed by DIP-MP/PIP. In the little finger, however, the most frequent sequences during flexion were PIP-DIP-MP followed by DIP-PIP-MP and during extension, DIP-MP/PIP followed by PIP/DIP-MP.
The purpose of this paper is to present wavelet-based noise removal (WBNR) techniques to remove noise from biomechanical acceleration signals obtained from numerical differentiation of displacement data. Manual and semiautomatic methods were used to determine thresholds for both orthogonal and biorthogonal filters. This study also compares the performance of WBNR approaches with four automatic conventional noise removal techniques used in biomechanics. The conclusion of this work is that WBNR techniques are very effective in removing noise from differentiated signals with sharp transients while leaving these transients intact. For biomechanical signals with certain characteristics, WBNR techniques perform better than conventional methods, as indicated by quantitative merit measures.
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