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Mary Emily Littrell, Young-Hui Chang and Brian P. Selgrade

difficult for less-experienced clinicians and often unreliable, 7 , 8 would be complemented by quantitative gait analysis. As medical devices and rehabilitation become more expensive, 9 it becomes even more important to quantify efficacy of these interventions to justify their cost. Here, we validate a

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Leilani A. Madrigal, Vincenzo Roma, Todd Caze, Arthur Maerlender and Debra Hope

exploratory factor analysis was necessary to explore the emergence of sports-related anxiety factors. Next, using a split-sample procedure, a confirmatory factor analysis was used to validate the factor structure from exploratory factor analysis to determine how well the model fit the data. In further

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Chunxiao Li, Lijuan Wang, Martin E. Block, Raymond K.W. Sum and Yandan Wu

-efficacy toward teaching students with ASD, it is important to validate a self-efficacy scale that can be used in China to proliferate the research in terms of including students with ASD. Self-Efficacy Scale for Including ASD The Physical Educators’ Self-Efficacy Toward Including Students with Disabilities

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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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Melissa M.B. Morrow, Bethany Lowndes, Emma Fortune, Kenton R. Kaufman and M. Susan Hallbeck

directly in traditional kinematic calculation algorithms. However, prior to widespread application of IMUs in the real world, the sensors and collection protocols need to be validated. Validation studies of IMUs used to capture upper and lower body kinematics are increasing. 14 – 30 To add to the body of

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Nathalie Alexander and Hermann Schwameder

While inclined walking is a frequent daily activity, muscle forces during this activity have rarely been examined. Musculoskeletal models are commonly used to estimate internal forces in healthy populations, but these require a priori validation. The aim of this study was to compare estimated muscle activity using a musculoskeletal model with measured EMG data during inclined walking. Ten healthy male participants walked at different inclinations of 0°, ± 6°, ± 12°, and ± 18° on a ramp equipped with 2 force plates. Kinematics, kinetics, and muscle activity of the musculus (m.) biceps femoris, m. rectus femoris, m. vastus lateralis, m. tibialis anterior, and m. gastrocnemius lateralis were recorded. Agreement between estimated and measured muscle activity was determined via correlation coefficients, mean absolute errors, and trend analysis. Correlation coefficients between estimated and measured muscle activity for approximately 69% of the conditions were above 0.7. Mean absolute errors were rather high with only approximately 38% being ≤ 30%. Trend analysis revealed similar estimated and measured muscle activities for all muscles and tasks (uphill and downhill walking), except m. tibialis anterior during uphill walking. This model can be used for further analysis in similar groups of participants.

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Lindsey Tulipani, Mark G. Boocock, Karen V. Lomond, Mahmoud El-Gohary, Duncan A. Reid and Sharon M. Henry

highly accurate systems (eg, electromagnetic motion tracking system, potentiometer); however, these studies did not attempt to collect data with human subjects performing functional tasks. 10 , 11 Clearly there exists a dearth of research that have attempted to validate inertial sensor data during

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Katherine L. Hsieh, Yaejin Moon, Vignesh Ramkrishnan, Rama Ratnam and Jacob J. Sosnoff

such sensors to be a valid measure of gait and static postural control. 17 – 19 However, these investigations did not use a body-tracking depth sensor to quantify VTC, which may provide an alternative approach to assess postural stability. Consequently, the aim of this study was to validate VTC

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John Goetschius, Mark A. Feger, Jay Hertel and Joseph M. Hart

-plates are considered the ‘gold-standard’ for postural control assessments, and have previously been utilized to validate novel postural control assessment devices. 3 – 5 Force-plates calculate COP excursions using load cells and measuring the 3-dimensional (x, y, z) forces and moments generated between

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Britton W. Brewer, Christine M. Caldwell, Albert J. Petitpas, Judy L. Van Raalte, Miquel Pans and Allen E. Cornelius

) item pool generation, (b) expert review, (c) administration of items to a development sample, (d) item evaluation, and (e) administration of scales to validation samples. Item Generation and Expert Review To facilitate the process of item pool generation, descriptions based on the review of identity