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procedure, for the smoothing process the precision was computed before each measurement after the corresponding calibration took place. B.5. Denoise of COP Signal B.5.1. COP is Distorted with White Noise The noise in the COP signal is the propagation noise arise from the combination of the components used to compute it with function f . Generally, it is modeled as a wide-band additive, stationary, zero-mean, and uncorrelated noise that contaminates the low-pass COP signal with noise variance Ï2 . However, even if the noise of the recorded GRF signals can be modeled as an additive zeromean âwhite noiseâ, the nonlinear transformation in COP computation destroys these properties to some extend (Woltring, 1995). The noise in the COP signal becomes non-stationary (i.e., unequal noise variance), except for the case where the Fz is constant (Fz = c). Therefore, noise stationarity may be true for stabilometric studies and digital low-pass filter or smoothing techniques can be used to remove high frequencies presented in the COP signal (Karlsson and Lanshammar, 1997; Woltring, 1986). The optimal cut-off frequency for the low-pass digital filter can be found by residual analysis (Winter and Patla, 1997) or by using the generalized, crossed-validation natural splines smoothing algorithm (GCVSPL) (Woltring, 1986). Natural splines of m th order behave like an m th order double Butterworth filter, where optimal cut-off frequency is the lowest frequency for which the residual noise is white (Woltring et al., 1987). Moreover, with sufficient oversampling is possible to retain significant signal components avoiding aliasing errors, while reducing noise level (FurnÃ©e, 1989a). The noise variance presented in a signal, or in its derivatives, after optimal smoothing depends on the band-limit of the signal and is proportional to the sampling rate and the variance of the inherent band-limited âwhite noiseâ presented in the raw data measurement (Lanshammar, 1982b), and is expressed as Ï2k = Ï2 Ï Ï2k+1 b Ï(2k + 1) (B.8) where Ï2k is the noise variance in the estimate k th order derivative Ï2 is the noise variance in the raw measured data (additive âwhite noiseâ) Ï is the sampling interval (Ï = 1 fs = 1 2ÏÏ0 with Ï0 â¥ 2Ï b ) Ï b is the band-limit of the signal k is the order of the derivative The term Ï2 Ï is known as spatiotemporal resolution criterion (Q ST ) and together with Shannon sampling theorem can be regarded as sufficient criteria in order to choose the sampling frequency Ï0 (FurnÃ©e, 1989a,b; Woltring, 1984, 1995). When a quantizing data acquisition system with 158