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John H. Challis

This article presents and evaluates a new procedure that automatically determines the cutoff frequency for the low-pass filtering of biomechanical data. The cutoff frequency was estimated by exploiting the properties of the autocorrelation function of white noise. The new procedure systematically varies the cutoff frequency of a Butterworth filter until the signal representing the difference between the filtered and unfiltered data is the best approximation to white noise as assessed using the autocorrelation function. The procedure was evaluated using signals generated from mathematical functions. Noise was added to these signals so mat they approximated signals arising from me analysis of human movement. The optimal cutoff frequency was computed by finding the cutoff frequency that gave me smallest difference between the estimated and true signal values. The new procedure produced similar cutoff frequencies and root mean square differences to me optimal values, for me zeroth, first and second derivatives of the signals. On the data sets investigated, this new procedure performed very similarly to the generalized cross-validated quintic spline.

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The authors and publisher regret that incorrect data were reported in JAB Volume 28, No. 2 (May 2012), on pages 222–227, in the article titled “Modeling the Stance Leg in Two-Dimensional Analyses of Sprinting: Inclusion of the MTP Joint Affect Joint Kinetics,” by Neil E. Bezodis, Aki I.T. Salo, and Grant Trewartha. An error in the data-processing script affected some of the calculated joint kinetics. The MTP plantar flexor moments were calculated correctly and are large enough to warrant consideration for a more complete picture of the energetics of sprinting. However, the correct data revealed that choice of foot model has relatively little effect on the calculated kinetics at other joints with the only meaningful differences being present in the ankle joint power and work data. As of November 1, 2012, the online version has been fully corrected and is available at jab-volume-28-issue-2-may.

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John de Grosbois and Luc Tremblay

additional final condition block (i.e., NV-1D-noTF) consisted of five trials and was solely added to confirm the presence of prism adaptation during the preceding NV-12D-TF condition. Ultimately, participants completed a total of 125 experimental trials. Data Processing and Analysis First, regarding the

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Aki Salo and Paul N. Grimshaw

Eight trials each of 7 athletes (4 women and 3 men) were videotaped and digitized in order to investigate the variation sources and kinematic variability of video motion analysis in sprint hurdles. Mean coefficients of variation (CVs) of individuals ranged from 1.0 to 92.2% for women and from 1.2 to 209.7% for men. There were 15 and 14 variables, respectively, in which mean CVs revealed less than 5% variation. In redigitizing, CVs revealed <1.0% for 12 variables for the women's trials and 10 variables for the men's trials. These results, together with variance components (between-subjects, within-subject, and redigitizing), showed that one operator and the analysis system together produced repeatable values for most of the variables. The most repeatable variables by this combination were displacement variables. However, further data processing (e.g., differentiation) appeared to have some unwanted effects on repeatability. Regarding the athletes' skill, CVs showed that athletes can reproduce most parts of their performance within certain (reasonably low) limits.

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Nelson Cortes and James Onate


Clinical assessment tools are needed to identify individual athletes who possess elevated risk for anterior cruciate ligament injury. Existing methods require expensive equipment and the investment of a large amount of time for data processing, which makes them unfeasible for preparticipation screening of a large number of athletes.


To assess the extent of agreement between LESS and the iLESS classifications of jump landing performance and the level of agreement between ratings assigned by a novice evaluator and an expert evaluator.


Ratings of drop-jump landings from 20 video recordings of NCAA Division I collegiate athletes, which were randomly selected from a large database.


The dichotomous iLESS score corresponded to the dichotomous classification of LESS score for 15 of 20 cases rated by the expert evaluator and 17 of 20 cases rated by the novice evaluator. For the iLESS, only 2 scores out of 20 differed between the evaluators.


A high level of agreement was observed between the LESS and iLESS methods for classification of jump- landing performance. Because the iLESS method is inexpensive and efficient, it may prove to be valuable for preparticipation assessment of knee injury risk.

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Timothy C. Sell, Mita T. Lovalekar, Takashi Nagai, Michael D. Wirt, John P. Abt and Scott M. Lephart

, Marlboro, MA) to a personal computer for additional signal and data processing. Procedures Participants reported to the Human Performance Research Laboratory for a single-test session. Dynamic and static postural stability were both assessed due to the lack of correlation observed in performance of these

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Antoine Falisse, Sam Van Rossom, Johannes Gijsbers, Frans Steenbrink, Ben J.H. van Basten, Ilse Jonkers, Antonie J. van den Bogert and Friedl De Groote

results obtained with different software systems, the added challenge is that discrepancies might result from differences between data processing workflows besides differences between models. To our knowledge, no studies have assessed differences in joint kinematics, kinetics, and muscle forces induced by

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James W. Roberts

is adopted for the sake of brevity and without conflating the principle objective of this study. There were 20 movement trials in the present study condition. Data Processing and Analysis Position data were filtered using a second-order, dual-pass Butterworth filter with a 10-Hz low-pass cutoff

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Damien Moore, Tania Pizzari, Jodie McClelland and Adam I. Semciw

actions) were performed for data normalization ( Supplementary Table S1 [available online]). Statistical Analysis The R statistical software package (version 3.4.1; ) was used for analysis. The EMG data processing has been described in detail previously. 6 Muscle activity

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Jonathan M. Williams, Michael Gara and Carol Clark

maintained for >2 seconds. One practice attempt was permitted prior to 3 hop landings being captured. The order of hopping was standardized to dominant prior to nondominant and forward hopping followed by medial and lateral hopping. Data Processing Data were transferred to MATLAB 2008b (The MathWorks, Inc