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Matthew S. Tenan, Andrew J. Tweedell and Courtney A. Haynes

movement. When considering surface EMG, this onset is commonly quantified via a linear envelope methodology proposed by David Winter. 1 Additional algorithmic approaches have also been validated for surface EMG onset detection, such as the Teager-Kaiser energy operator 2 and Sample Entropy. 3

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Ryan M. Chambers, Tim J. Gabbett and Michael H. Cole

, snow sports, individual sports, and water sports. 11 Validated algorithms have been applied to microsensor data to automate the collection of sport-specific movements, such as fast bowling in cricket, 12 pitching in baseball, 13 and tackling in rugby. 11 , 14 , 15 To date, researchers have used

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Aisha Chen, Sandhya Selvaraj, Vennila Krishnan and Shadnaz Asgari

applied a threshold equivalent to 10% of maximal COP velocity to calculate the onset. Nevertheless, many studies commonly use the tedious visual inspection of COP velocity or displacement to obtain the gait onset when the chosen algorithm fails to detect the correct onset. 20 , 21 , 25 Evaluation of both

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Oren Tirosh and W.A. Sparrow

Analysis of human gait requires accurate measurement of foot-ground contact, often determined using either foot-ground reaction force thresholds or kinematic data. This study examined the differences in calculating event times across five vertical force thresholds and validated a vertical acceleration-based algorithm as a measure of heel contact and toe-off. The experiment also revealed the accuracy in determining heel contact and toe-off when raw displacement/time data were smoothed using a range of digital filter cutoff frequencies. Four healthy young participants completed 10 walking trials: 5 at normal speed (1.2 m/s) and 5 at fast speed (1.8 m/s). A 3D optoelectric system was synchronized with a forceplate to measure the times when vertical force exceeded (heel contact) or fell below (toe-off) 10, 20, 30, 40, and 50 N. These were then compared and subsequently used to validate an acceleration-based method for calculating heel contact and toe-off with the displacement/time data filtered across a range of four cutoff frequencies. Linear regression analyses showed that during both normal and fast walking, any force threshold within 0 to 50 N could be used to predict heel-contact time. For estimating toe-off low force thresholds, 10 N or less should be used. When raw data were filtered with the optimal cutoff frequency, the absolute value (AbsDt) of the difference between the forceplate event times obtained using a 10-N threshold and the event times of heel contact and toe-off using the acceleration-based algorithms revealed average AbsDt of 10.0 and 16.5 ms for normal walking, and 7.4 and 13.5 ms for fast walking. Data smoothing with the non-optimal cutoff frequencies influenced the event times computed by the algorithms and produced greater AbsDt values. Optimal data filtering procedures are, therefore, essential for ensuring accurate measures of heel contact and toe-off when using the acceleration-based algorithms.

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Maarten Beek, Carolyn F. Small, Randy E. Ellis, Richard W. Sellens and David R. Pichora

Computer assisted surgical interventions and research in joint kinematics rely heavily on the accurate registration of three-dimensional bone surface models reconstructed from various imaging technologies. Anomalous results were seen in a kinematic study of carpal bones using a principal axes alignment approach for the registration. The study was repeated using an iterative closest point algorithm, which is more accurate, but also more demanding to apply. The principal axes method showed errors between 0.35 mm and 0.49 mm for the scaphoid, and between 0.40 mm and 1.22 mm for the pisiform. The iterative closest point method produced errors of less than 0.4 mm. These results show that while the principal axes method approached the accuracy of the iterative closest point algorithm in asymmetrical bones, there were more pronounced errors in bones with some symmetry. Principal axes registration for carpal bones should be avoided.

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Karen B. Dorsey, Jeph Herrin and Harlan M. Krumholz

An algorithm was developed to describe how physical activity (PA) patterns relate to overall motion counts. Thirty-five children wore an accelerometer (7-days). Each motion count was compared with the mean of surrounding counts within 21 min. Counts per minute similar to the mean were grouped into bouts. Counts that differed by more than 20% of the coefficient of variations (based on the mean and SD of the 21 min period) indicated transitions between bouts. Children with more daily motion had more and longer moderate (MPA) and vigorous (VPA) bouts, higher counts during MPA bouts, and more transitions from VPA to VPA bouts. In addition to differences in PA levels, highly active and less active children perform PA differently.

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Ricardo Pires, Thays Falcari, Alexandre B. Campo, Bárbara C. Pulcineli, Joseph Hamill and Ulysses Fernandes Ervilha

signals in the different conditions. A machine-learning algorithm may be able to relate EMG signals to running conditions using a known set of EMG data (training phase) and to deduce what is the running condition when a new set of EMG data is presented to the machine (classification phase). A support

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John R. Sirard, Ann Forsyth, J. Michael Oakes and Kathryn H. Schmitz

Background:

The purpose of this study was to determine 1) the test-retest reliability of adult accelerometer-measured physical activity, and 2) how data processing decisions affect physical activity levels and test-retest reliability.

Methods:

143 people wore the ActiGraph accelerometer for 2 7-day periods, 1 to 4 weeks apart. Five algorithms, varying nonwear criteria (20 vs. 60 min of 0 counts) and minimum wear requirements (6 vs. 10 hrs/day for ≥ 4 days) and a separate algorithm requiring ≥ 3 counts per min and ≥ 2 hours per day, were used to process the accelerometer data.

Results:

Processing the accelerometer data with different algorithms resulted in different levels of counts per day, sedentary, and moderate-to-vigorous physical activity. Reliability correlations were very good to excellent (ICC = 0.70−0.90) for almost all algorithms and there were no significant differences between physical activity measures at Time 1 and Time 2.

Conclusions:

This paper presents the first assessment of test-retest reliability of the Actigraph over separate administrations in free-living subjects. The ActiGraph was highly reliable in measuring activity over a 7-day period in natural settings but data were sensitive to the algorithms used to process them.

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Christopher Joyce, Angus Burnett and Miccal Matthews

No method currently exists to determine the location of the kick point during the golf swing. This study consisted of two phases. In the first phase, the static kick point of 10 drivers (having identical grip and head but fitted with shafts of differing mass and stiffness) was determined by two methods: (1) a visual method used by professional club fitters and (2) an algorithm using 3D locations of markers positioned on the golf club. Using level of agreement statistics, we showed the latter technique was a valid method to determine the location of the static kick point. In phase two, the validated method was used to determine the dynamic kick point during the golf swing. Twelve elite male golfers had three shots analyzed for two drivers fitted with stiff shafts of differing mass (56 g and 78 g). Excellent between-trial reliability was found for dynamic kick point location. Differences were found for dynamic kick point location when compared with static kick point location, as well as between-shaft and within-shaft. These findings have implications for future investigations examining the bending behavior of golf clubs, as well as being useful to examine relationships between properties of the shaft and launch parameters.

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Patti Syvertson, Emily Dietz, Monica Matocha, Janet McMurray, Russell Baker, Alan Nasypany, Don Reordan and Michael Paddack

Context:

Achilles tendinopathy is relatively common in both the general and athletic populations. The current gold standard for the treatment of Achilles tendinopathy is eccentric exercise, which can be painful and time consuming. While there is limited research on indirect treatment approaches, it has been proposed that tendinopathy patients do respond to indirect approaches in fewer treatments without provoking pain.

Objective:

To determine the effectiveness of using a treatment-based-classification (TBC) algorithm as a strategy for classifying and treating patients diagnosed with Achilles tendinopathy.

Participants:

11 subjects (mean age 28.0 ±15.37 y) diagnosed with Achilles tendinopathy.

Design:

Case series.

Setting:

Participants were evaluated, diagnosed, and treated at multiple clinics.

Main Outcome Measures:

Numeric Rating Scale (NRS), Disablement in the Physically Active Scale (DPA Scale), Victorian Institute of Sport Assessment–Achilles (VISA-A), Global Rating of Change (GRC), and Nirschl Phase Rating Scale were recorded to establish baseline scores and evaluate participant progress.

Results:

A repeated-measures ANOVA was conducted to analyze NRS scores from initial exam to discharge and at 1-mo follow-up. Paired t tests were analyzed to determine the effectiveness of using a TBC algorithm from initial exam to discharge on the DPA Scale and VISA-A. Descriptive statistics were evaluated to determine outcomes as reported on the GRC.

Conclusion:

The results of this case series provide evidence that using a TBC algorithm can improve function while decreasing pain and disability in Achilles tendinopathy participants.