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Rosanna Gilderthorp, Jan Burns and Fergal Jones

severity of impairment that athletes may present with. However, to develop a more stratified approach, similar to other impairment groups, research must be undertaken, upon which a more sensitive classification system could be based. It is the purpose of this paper to explore how such a system could be

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David S. Haydon, Ross A. Pinder, Paul N. Grimshaw and William S.P. Robertson

classifications, 1 as well as performance outcomes. 2 Despite an increase in popularity and research in wheelchair rugby (WCR), there is currently a limited understanding of how the level of activity limitation affects key kinematic variables and their impact on chair acceleration and sprint performance

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Tiago M. Barbosa, Jorge E. Morais, Mário J. Costa, José Goncalves, Daniel A. Marinho and António J. Silva

The aim of this article has been to classify swimmers based on kinematics, hydrodynamics, and anthropometrics. Sixty-seven young swimmers made a maximal 25 m front-crawl to measure with a speedometer the swimming velocity (v), speed-fluctuation (dv) and dv normalized to v (dv/v). Another two 25 m bouts with and without carrying a perturbation device were made to estimate active drag coefficient (CD a). Trunk transverse surface area (S) was measured with photogrammetric technique on land and in the hydrodynamic position. Cluster 1 was related to swimmers with a high speed fluctuation (ie, dv and dv/v), cluster 2 with anthropometrics (ie, S) and cluster 3 with a high hydrodynamic profile (ie, CD a). The variable that seems to discriminate better the clusters was the dv/v (F = 53.680; P < .001), followed by the dv (F = 28.506; P < .001), CD a (F = 21.025; P < .001), S (F = 6.297; P < .01) and v (F = 5.375; P = .01). Stepwise discriminant analysis extracted 2 functions: Function 1 was mainly defined by dv/v and S (74.3% of variance), whereas function 2 was mainly defined by CD a (25.7% of variance). It can be concluded that kinematics, hydrodynamics and anthropometrics are determinant domains in which to classify and characterize young swimmers’ profiles.

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Rienk M.A. van der Slikke, Annemarie M.H. de Witte, Monique A.M. Berger, Daan J.J. Bregman and Dirk Jan H.E.J. Veeger

high seating position for shooting and its assumed negative effect on WMP. The conditions for this trade-off are highly individual, specified by the athlete’s classification, skills, and field position. A prerequisite to quantify the relationship between performance and wheelchair settings is to have

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Anna Pulakka, Eric J. Shiroma, Tamara B. Harris, Jaana Pentti, Jussi Vahtera and Sari Stenholm

studies have examined the effects of sleep and non-wear algorithms on the classification of sleep, non-wear, and sedentary time based on wrist measurement ( Migueles et al., 2017 ; Schrack et al., 2016 ). To address these gaps in the literature, we used accelerometers worn on wrist for 24 hours/day to

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James M. Rhodes, Barry S. Mason, Thomas A.W. Paulson and Victoria L. Goosey-Tolfrey

has revealed that WCR is an intermittent sport with players typically covering distances of 2500 and 4600 m during competition 3 , 4 and the majority of time spent performing low-speed activities interspersed with frequent bouts of high-speed activities. 3 Classification has also been shown to

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Raúl Reina, Aitor Iturricastillo, Rafael Sabido, Maria Campayo-Piernas and Javier Yanci

among IFCPF functional classes, demonstrating the usability of those tests for FPCP evaluation. Methods Participants A total of 132 international parafootballers (age = 25.8 [6.7] y; training experience = 10.7 [7.5] y) classified according to the IFCPF classification rules participated in this study

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Kirsti Van Dornick and Nancy L.I. Spencer

their bodies’ degree of function to determine who they will compete with and against ( International Paralympic Committee [IPC], 2015a , 2015b ; Steadward & Peterson, 1997 ). Classification provides a structure for competition in parasport ( IPC, 2015a ; Tweedy, Beckman, & Connick, 2014 ), with the

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Joshua Twaites, Richard Everson, Joss Langford and Melvyn Hillsdon

focusing on the raw data are becoming more commonplace. As acceleration is only a surrogate of the true PA performed, methods for converting the data into behavioral metrics are required. A technique of growing popularity is activity classification via machine learning, which creates a model, based on some

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Yves C. Vanlandewijck, Christina Evaggelinou, Daniel D. Daly, Siska Van Houtte, Joeri Verellen, Vanessa Aspeslagh, Robby Hendrickx, Tine Piessens and Bjorn Zwakhoven

The player classification system in wheelchair basketball (composed of four classes) is based on an analysis of players’ functional resources through game observation and field-testing. This study examines if the classes are in the correct proportion relative to each other. During the Wheelchair Basketball World Championships in Sydney 1998, 12 teams were videotaped for three 40-min games. Eighty-eight male players were retained for a detailed performance analysis by means of the Comprehensive Basketball Grading System (CBGS). Although a slight underestimation of the functional potential of Class II and III players was noted, it was concluded that the player classification system in wheelchair basketball proportionally represents the functional potential of the players.