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81.
John Warmenhoven Stephen Cobley Conny Draper Andrew Harrison Norma Bargary Richard Smith 《Sports biomechanics / International Society of Biomechanics in Sports》2019,18(3):317-341
The proliferation of new biomechanical technology in laboratory and field settings facilitates the capture of data-sets consisting of complex time-series. An understanding of the appropriate statistical approaches for analysing and interpreting these data-sets is required and the functional data analysis (FDA) family of statistical techniques has emerged in the biomechanical literature. Given the use of FDA is currently in its infancy with biomechanical data, this paper will form the first of a two part series aiming to address practical issues surrounding the application of FDA techniques in biomechanics. This work focuses on functional principal components analysis (fPCA), which is explored using existing literature and sample data from an on-water rowing database. In particular methodological considerations for the implementation of fPCA such as temporal normalisation of data, removal of unwanted forms of variation in a data-set and documented methods for preserving the original temporal properties within a set of curves are explored in detail as a part of this review. Limitations and strengths of the technique are outlined and recommendations are provided to encourage the appropriate use of fPCA within the field of applied sports biomechanics. 相似文献
82.
Harry F. Dorrell Joseph M. Moore Mark F. Smith Thomas I. Gee 《Journal of sports sciences》2019,37(1):67-73
This study investigated the validity and reliability of the GymAware PowerTool (GPT). Thirteen resistance trained participants completed three visits, consisting of three repetitions of free-weight back squat, bench press, deadlift (80% one repetition maximum), and countermovement jump. Bar displacement, peak and mean velocity, peak and mean force, and jump height were calculated using the GPT, a three-dimensional motion capture system (Motion Analysis Corporation; 150 Hz), and a force plate (Kistler; 1500 Hz). Least products regression were used to compare agreeability between devices. A within-trial one-way ANOVA, typical error (TE; %), and smallest worthwhile change (SWC) were used to assess reliability. Regression analysis resulted in R2 values of >0.85 for all variables excluding deadlift mean velocity (R2 = 0.54–0.69). Significant differences were observed between visits 3-2 for bench press bar displacement (0.395 ± 0.055 m; 0.383 ± 0.053 m), and deadlift bar displacement (0.557 ± 0.034 m; 0.568 ± 0.034 m). No other significant differences were found. Low to moderate TE (0.6–8.8%) were found for all variables, with SWC ranging 1.7–7.4%. The data provides evidence that the GPT can be used to measure kinetic and kinematic outputs, however, care should be taken when monitoring deadlift performance. 相似文献
83.
Liezel Hurter Alex V. Rowlands Stuart J. Fairclough Karl C. Gibbon Zoe R. Knowles Lorna A. Porcellato 《Journal of sports sciences》2019,37(16):1910-1918
This study aimed to validate the Sedentary Sphere posture classification method from wrist-worn accelerometers in children. Twenty-seven 9–10-year-old children wore ActiGraph GT9X (AG) and GENEActiv (GA) accelerometers on both wrists, and activPAL on the thigh while completing prescribed activities: five sedentary activities, standing with a phone, walking (criterion for all 7: observation) and 10-min free-living play (criterion: activPAL). In an independent sample, 21 children wore AG and GA accelerometers on the non-dominant wrist and activPAL for two days of free-living. Per cent accuracy, pairwise 95% equivalence tests (±10% equivalence zone) and intra-class correlation coefficients (ICC) analyses were completed. Accuracy was similar, for prescribed activities irrespective of brand (non-dominant wrist: 77–78%; dominant wrist: 79%). Posture estimates were equivalent between wrists within brand (±6%, ICC > 0.81, lower 95% CI ≥ 0.75), between brands worn on the same wrist (±5%, ICC ≥ 0.84, lower 95% CI ≥ 0.80) and between brands worn on opposing wrists (±6%, ICC ≥ 0.78, lower 95% CI ≥ 0.72). Agreement with activPAL during free-living was 77%, but sedentary time was underestimated by 7% (GA) and 10% (AG). The Sedentary Sphere can be used to classify posture from wrist-worn AG and GA accelerometers for group-level estimates in children, but future work is needed to improve the algorithm for better individual-level results. 相似文献
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James A. H. Smith Alexander D. McKerrow Tertius A. Kohn 《Journal of sports sciences》2017,35(16):1592-1597
Exercise testing on motorised treadmills provides valuable information about running performance and metabolism; however, the impact of treadmill type on these tests has not been investigated. This study compared the energy demand of running on two laboratory treadmills: an HP Cosmos (C) and a Quinton (Q) model, with the latter having a 4.5 times stiffer running platform. Twelve experienced runners ran identical bouts on these treadmills at a range of four submaximal velocities (reported data is for the velocity that approximated 75–81% VO2max). The stiffer treadmill elicited higher oxygen consumption (C: 46.7 ± 3.8; Q: 50.1 ± 4.3 ml·kg?1 · min?1), energy expenditure (C: 16.0 ± 2.5; Q: 17.7 ± 2.9 kcal · min?1), carbohydrate oxidation (C: 9.6 ± 3.1; Q: 13.0 ± 3.9 kcal · min?1), heart rate (C: 155 ± 16; Q: 163 ± 16 beats · min?1) and rating of perceived exertion (C: 13.8 ± 1.2; Q: 14.7 ± 1.2), but lower fat oxidation (C: 6.4 ± 2.3; Q: 4.6 ± 2.5 kcal · min?1) (all analysis of variance treadmill comparisons P < 0.01). This study confirms that caution is required when comparing performance and metabolic results between different treadmills and suggests that treadmills will vary in their comparability to over-ground running depending on the running platform stiffness. 相似文献
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