Analysis of ground reaction force and electromyographic activity of the gastrocnemius muscle during double support

O documento em anexo encontra-se na versão post-print (versão corrigida pelo editor).Purpose: Mechanisms associated with energy expenditure during gait have been extensively researched and studied. According to the double-inverted pendulum model energy expenditure is higher during double support, as lower limbs need to work to redirect the centre of mass velocity. This study looks into how the ground reaction force (GRF) of one limb affects the muscle activity required by the medial gastrocnemius (MG) of the contralateral limb during step-to-step transition. Methods: Thirty-five subjects were monitored as to the MG electromyographic activity (EMGa) of one limb and the GRF of the contralateral limb during double support. Results: After determination of the Pearson correlation coefficient (r), a moderate correlation was observed between the MG EMGa of the dominant leg and the vertical (Fz) and anteroposterior (Fy) components of GRF of the non-dominant leg (r=0.797, p<0.0001; r=-0.807, p<0.0001) and a weak and moderate correlation was observed between the MG EMGa of the non-dominant leg and the Fz and Fy of the dominant leg, respectively (r=0.442, p=0.018; r=-0.684 p<0.0001). Conclusions: The results obtained suggest that during double support, GRF is associated with the EMGa of the contralateral MG and that there is an increased dependence between the GRF of the non-dominant leg and the EMGa of the dominant MG

A comparison of methods for denoising of well test pressure data

Abstract Pressure transient data from downhole gauges are one of the key parameters in characterizing reservoir properties and forecasting future reservoir performance. Reservoir pressure is usually measured under dynamic changes. The collected data usually contain different levels of noise, particularly due to imperfections in measuring instruments and imperfect calibrations. The latter is due to changes between the laboratory environment and reservoir conditions. To have accurate descriptions of reservoir, it is essential to smooth the pressure data. Most related studies have employed the wavelet transform to reduce noise. However, there appears to be little research addressing the use of other smoothing techniques for pressure transient data. This paper, therefore, evaluates and compares the performance of three types of smoothing and noise removal methods, namely wavelet transform as a widely used filtering technique, regression-based smoothers, and autoregressive smoothing methods to reduce artificial noise added to simulated dual-porosity pressure data. Particularly, noise is more pronounced in pressure derivative, and so denoising of pressure derivative requires more effective tools. The effectiveness of the noise removing methods was compared using mean square error. The results show that the regression-based methods lead to the same or even better reduction in the noise level as compared to the wavelet domain filter, while the employed autoregressive method results in a moderate performance. We also test the performance of various combinations of the different smoothing methods to filter the same noisy data. It is shown that the combined locally weighted scatterplot smooth (LOESS) and autoregressive moving average (ARMA) gives the best smoothing performance for pressure derivative data. Application of the combined LOESS–ARMA to real field data shows promising results