A marker has to be seen by at least two cameras for its three-dimensional (3-D) reconstruction, and the accuracy can be improved with more cameras. However, a change in the set of cameras used in the reconstruction can alter the kinematics. The purpose of this study was to quantify the harmful effect of occlusions on two-dimensional (2-D) images and to make recommendations about the signal processing. A reference kinematics data set was collected for a three degree-of-freedom linkage with three cameras of a commercial motion analysis system without any occlusion on the 2-D images. In the 2-D images, some occlusions were artificially created based on trials of real cyclic motions. An interpolation of 2-D trajectories before the 3-D reconstruction and two filters (Savitsky–Golay and Butterworth filters) after reconstruction were successively applied to minimize the effect of the 2-D occlusions. The filter parameters were optimized by minimizing the root mean square error between the reference and the filtered data. The optimal parameters of the filters were marker dependent, whereas no filter was necessary after a 2-D interpolation. As the occlusions cause systematic error in the 3-D reconstruction, the interpolation of the 2-D trajectories is more appropriate than filtering the 3-D trajectories.
Mickaël Begon and Patrick Lacouture
Patrick Ippersiel, Richard Preuss and Shawn M. Robbins
? J Psychiatr Res . 1996 ; 30 ( 6 ): 483 – 492 . PubMed ID: 9023792 doi:10.1016/S0022-3956(96)00033-7 9023792 10.1016/S0022-3956(96)00033-7 13. Klingspor M . Hilbert Transform: Mathematical Theory and Applications to Signal Processing [master’s thesis]. Linköping, Sweden : Linköping University
Francisco Javier Alonso, Publio Pintado and José María Del Castillo
The use of the Hodrick-Prescott (HP) filter is presented as an alternative to the traditional digital filtering and spline smoothing methods currently used in biomechanics. In econometrics, HP filtering is a standard tool used to decompose a macroeconomic time series into a nonstationary trend component and a stationary residual component. The use of the HP filter in the present work is based on reasonable assumptions about the jerk and noise components of the raw displacement signal. Its applicability was tested on 4 kinematic signals with different characteristics. Two are well known signals taken from the literature on biomechanical signal filtering, and the other two were acquired with our own motion capture system. The criterion for the selection of cutoff frequency was based on the power spectral density of the raw displacement signals. The results showed the technique to be well suited to filtering biomechanical displacement signals in order to obtain accurate higher derivatives in a simple and systematic way. Namely, the HP filter and the generalized cross-validated quintic spline (GCVSPL) produce similar RMS errors on the first (0.1063 vs. 0.1024 m/s2) and second (23.76 vs. 23.24 rad/s2) signals. The HP filter performs slightly better than GCVSPL on the third (0.209 vs. 0.236 m/s2) and fourth (1.596 vs. 2.315 m/s2) signals.
Bing Yu, David Gabriel, Larry Noble and Kai-Nan An
The purposes of this study were (a) to develop a procedure for objectively determining the optimum cutoff frequency for the Butterworth low-pass digital filler, and (b) to evaluate the cutoff frequencies derived from the residual analysis. A set of knee flexion-extension angle data in normal gait was used as the standard data set. The standard data were sampled at different sampling frequencies. Random errors with different magnitudes were added to the standard data to create different sets of raw data with a given sampling frequency. Each raw data set was filtered through a Butterworth low-pass digital filter at different cutoff frequencies. The cutoff frequency corresponding to the minimum error in the second time derivatives for a given set of raw data was considered as the optimum for that set of raw data. A procedure for estimating the optimum cutoff frequency from the sampling frequency and estimated relative mean error in the raw data set was developed. The estimated optimum cutoff frequency significantly correlated to the true optimum cutoff frequency with a correlation determinant value of 0.96. This procedure was applied to estimate the optimum cutoff frequency for another set of kinematic data. The calculated accelerations of the filtered data essentially matched the measured acceleration curve. There is no correlation between the cutoff frequency derived from the residual analysis and the true optimum cutoff frequency. The cutoff frequencies derived from the residual analysis were significantly lower than the optimum, especially when the sampling frequency is high.
Bernd J. Stetter, Erica Buckeridge, Vinzenz von Tscharner, Sandro R. Nigg and Benno M. Nigg
This study presents a new approach for automated identification of ice hockey skating strides and a method to detect ice contact and swing phases of individual strides by quantifying vibrations in 3D acceleration data during the blade–ice interaction. The strides of a 30-m forward sprinting task, performed by 6 ice hockey players, were evaluated using a 3D accelerometer fixed to a hockey skate. Synchronized plantar pressure data were recorded as reference data. To determine the accuracy of the new method on a range of forward stride patterns for temporal skating events, estimated contact times and stride times for a sequence of 5 consecutive strides was validated. Bland-Altman limits of agreement (95%) between accelerometer and plantar pressure derived data were less than 0.019 s. Mean differences between the 2 capture methods were shown to be less than 1 ms for contact and stride time. These results demonstrate the validity of the novel approach to determine strides, ice contact, and swing phases during ice hockey skating. This technology is accurate, simple, effective, and allows for in-field ice hockey testing.
Ricardo Pires, Thays Falcari, Alexandre B. Campo, Bárbara C. Pulcineli, Joseph Hamill and Ulysses Fernandes Ervilha
comprehensive measurement approach . Int J Sports Med . 1987 ; 8 ( 3 ): 196 – 202 . PubMed ID: 3623781 doi:10.1055/s-2008-1025655 10.1055/s-2008-1025655 3623781 6. Hermens HJ , Merletti R , Rix H , Freriks B . The State of the Art on Signal Processing Methods for Surface Electromyography
John Goetschius, Mark A. Feger, Jay Hertel and Joseph M. Hart
/software, the signal processing and variable calculations were unique to each device. Additionally, our decision to ‘stack’ the pressure-mat on top of the force-plate may have altered the measurement values for the force-plate. This decision was made to limit the effects of variability between individual
Esther Casas, Arturo Justes and Carlos Calvo
intraclass correlation coefficient of the RMS values of EMG signals was smaller than .70 7 between the 2 measurements of the same muscle or of its antagonist in the different positions. Another criterion to rule out participants was RMS of resting values larger than 7 μV. 8 Statistical Analysis Signals
Jairo H. Migueles, Alex V. Rowlands, Florian Huber, Séverine Sabia and Vincent T. van Hees
gained momentum since the 1990s. In the beginning, wearable movement sensors (i.e., accelerometers) typically performed onboard signal processing and only stored derived output to reduce battery consumption and memory requirements. However, following a general movement towards more transparent and open
Daniel J. Plews, Ben Scott, Marco Altini, Matt Wood, Andrew E. Kilding and Paul B. Laursen
was fitted just below V6. Photoplethysmography PPG was acquired via a commercially available smartphone application known as “HRV4training” (Amsterdam, Netherlands; see http://www.hrv4training.com/ ). Given the low frame rate of mobile phone cameras, different signal processing techniques should be