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A new class of spatial filters for surface electromyographic (EMG) signal detection is proposed. These filters are based on the 2-D spatial wavelet decomposition of the surface EMG recorded with a grid of electrodes and inverse transformation after zeroing a subset of the transformation coefficients. The filter transfer function depends on the selected mother wavelet in the two spatial directions. Wavelet parameterization is proposed with the aim of signal-based optimization of the transfer function of the spatial filter. The optimization criterion was the minimization of the entropy of the time samples of the output signal. The optimized spatial filter is linear and space invariant. In simulated and experimental recordings, the optimized wavelet filter showed increased selectivity with respect to previously proposed filters. For example, in simulation, the ratio between the peak-to-peak amplitude of action potentials generated by motor units 20[degrees] apart in the transversal direction was 8.58% (with monopolar recording), 2.47% (double differential), 2.59% (normal double differential), and 0.47% (optimized wavelet filter). In experimental recordings, the duration of the detected action potentials decreased from (mean [+ or -] SD) 6.9 [+ or -] 0.3 ms (monopolar recording), to 4.5 [+ or -] 0.2 ms (normal double differential), 3.7 [+ or -] 0.2 ms (double differential), and 3.0 [+ or -] 0.1 ms (optimized wavelet filter). In conclusion, the new class of spatial filters with the proposed signal-based optimization of the transfer function allows better discrimination of individual motor unit activities in surface EMG recordings than it was previously possible. Index Terms--Motor unit, selectivity, spatial filtering, surface electromyography (EMG), wavelet design.

The present work proposes the realization of a parametric/variational CAD model of a normotype lumbar vertebra, which could be used for improving the effectiveness of actual imaging techniques in informational augmentation of the orthopaedic and traumatological diagnosis. In addition it could be used for ergonomic static and dynamical analysis of the lumbar region and vertebral column.

Predictive models, which enable the prediction of nanocomposite properties from their morphologies and account for polymer orientation, could greatly assist the exploitation of this new class of materials in more diversified and demanding market fields. This article focuses on the prediction of effective elastic properties (Young's moduli) of polymer nanocomposite films (copolyamide-6/nanoclay) using 3D analytical (based on the Mori-Tanaka theory) and 3D finite element (FE) models. The analytical model accounts for the orientation of polymer chains induced by drawing. 3D FE model exploits the representative volume element concept and accounts for the nanocomposite morphology as determined from transmission electron microscopy experiments. Model predictions were compared with experimental results obtained for nanocomposite films produced by means a pilot-scale film blowing equipment and collected at different draw ratios. © 2011 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys, 2012 [ABSTRACT FROM AUTHOR]