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Mickaël Begon and Patrick Lacouture

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.

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Daniel J. Davis and John H. Challis

) Error time series for each of the different signal processing approaches along with the %RMSE, Absolute %Impact Peak Error, and Geers %Phase Error values for the specific estimate depicted graphically in panel (A). %RMSE indicates percent root mean square error; SSF-BS, signal-section filtering by body

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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

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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.

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Joshua Twaites, Richard Everson, Joss Langford, and Melvyn Hillsdon

Purpose: Physical activity classifiers are typically trained on data obtained from sensors at a set orientation. Changes in this orientation (such as being on a different wrist) result in performance degradation. This work investigates a method to obtain sensor location and orientation invariance for classification of wrist-mounted accelerometry via a technique known as domain adaption. Methods: Data was gathered from 16 participants who wore accelerometers on both wrists. Physical activity classification models were created using data from each wrist and then used to predict activities when using data from the opposing wrist. Using subspace alignment domain adaption, this procedure was then repeated to align the training and testing data before the classification stage. Results: Prediction of activity when using data where the wearer’s wrist was incorrectly specified resulted in a significant (p = .01) decrease in performance of 12%. When using domain adaption this drop in performance became negligible (M difference < 1%, p = .73). Conclusion: Domain adaption is a valuable method for achieving accurate physical activity classification independent of sensor orientation in wrist-worn accelerometry.

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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.

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Becky Breau, Hannah J. Coyle-Asbil, and Lori Ann Vallis

The purpose of this scoping review was to examine publications using accelerometers in children aged 6 months to <6 years and report on current methodologies used for data collection and analyses. We examined device make and model, device placement, sampling frequency, data collection protocol, definition of nonwear time, inclusion criteria, epoch duration, and cut points. Five online databases and three gray literature databases were searched. Studies were included if they were published in English between January 2009 and March 2021. A total of 627 articles were included for descriptive analyses. Of the reviewed articles, 75% used ActiGraph devices. The most common device placement was hip or waist. More than 80% of articles did not report a sampling frequency, and 7-day protocols during only waking hours were the most frequently reported. Fifteen-second epoch durations and the cut points developed by Pate et al. in 2006 were the most common. A total of 203 articles did not report which definition of nonwear time was used; when reported, “20 minutes of consecutive zeros” was the most frequently used. Finally, the most common inclusion criteria were “greater or equal to 10 hr/day for at least 3 days” for studies conducted in free-living environments and “greater than 50% of the school day” for studies conducted in preschool or childcare environments. Results demonstrated a major lack of reporting of methods used to analyze accelerometer data from young children. A list of recommended reporting practices was developed to encourage increased reporting of key methodological details for research in this area.

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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.

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Joshua Twaites, Richard Everson, Joss Langford, and Melvyn Hillsdon

Introduction: Data from wrist-worn accelerometers often has an inherent natural segmentation that reflects transitioning from one activity to another. The aim of this study was to develop an activity transition detection method to realize this natural segmentation. Methods: Data was gathered from 16 participants who wore triaxial wrist accelerometers in a lab-based protocol and 47 participants in a free-living protocol. Change point detection was used to create a method for detecting activity transitions. The agreement between observed and predicted transitions was assessed by the Matthews Correlation Coefficient (MCC), Root Mean Squared Error (RMSE), and two additional metrics created for this task; the Ratio of Minimum Mean Distance (RMMD) and the Ratio of Sensitivity (RoS). The effects of varying combinations of acceleration axes were also investigated to determine the most effective set of axes. A novel post-processing technique was developed to mitigate a major limitation identified in current transition detection methods. Results: The developed transition detection method achieved a MCC of 0.763, a RMSE of 3.17, a RoS of 2.40, and a RMMD of 3.21, outperforming existing techniques. The post-processing technique developed improved the performance of all methods when identifying transitions. It was found that using solely the y-axis (vertical acceleration) allowed for optimal performance. Conclusion: Change point detection is a valid method for identifying transitions in activity using wrist-worn accelerometer data. The new post processing technique developed improves the performance of transition detection methods.

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Paul R. Hibbing, Seth A. Creasy, and Jordan A. Carlson

Physical behaviors (e.g., sleep, sedentary behavior, and physical activity) often occur in sustained bouts that are punctuated with brief interruptions. To detect and classify these interrupted bouts, researchers commonly use wearable devices and specialized algorithms. Most algorithms examine the data in chronological order, initiating and terminating bouts whenever specific criteria are met. Consequently, the bouts may encapsulate or overlap with later periods that also meet the activation and termination criteria (i.e., alternative bout solutions). In some cases, it is desirable to compare these alternative bout solutions before making a final classification. Thus, comparison-focused algorithms are needed, which can be used in isolation or in concert with their chronology-focused counterparts. In this technical note, we present a comparison-focused algorithm called CRIB (Clustered Recognition of Interrupted Bouts). It uses agglomerative hierarchical clustering to facilitate the comparison of different bout solutions, with the final classification being made in favor of the smallest number of bouts that comply with user-specified criteria (i.e., limits on the number, individual duration, and cumulative duration of interruptions). For demonstration, we use CRIB to assess bouts of moderate to vigorous physical activity in accelerometer data from the National Health and Nutrition Examination Survey, and we include a comparison against results from two established chronology-focused algorithms. Our discussion explores strengths and limitations of CRIB, as well as potential considerations and applications for using it in future studies. An online vignette (https://github.com/paulhibbing/PBpatterns/blob/main/vignettes/CRIB.pdf) is available to assist users with implementing CRIB in R.