Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
In Paralympic cross-country sit skiing, athlete classification is performed by an expert panel, so it may be affected by subjectivity. An evidence-based classification is required, in which objective measures of impairment must be identified. The purposes of this study were: (i) to evaluate the reliability of 5 trunk strength measures and 18 trunk control measures developed for the purposes of classification; (ii) to rank the objective measures, according to the largest effects on performance. Using a new testing device, 14 elite sit-skiers performed two upright seated press tests and one simulated poling test to evaluate trunk strength. They were also subjected to unpredictable balance perturbations to measure trunk control. Tests were repeated on two separate days and test-retest reliability of trunk strength and trunk control measures was evaluated. A cluster analysis was run and correlation was evaluated, including all strength and control measures, to identify the measures that contributed most to clustering participants. Intraclass correlations coefficients (ICC) were 0.71 < ICC < 0.98 and 0.83 < ICC < 0.99 for upright seated press and perturbations, respectively. Cluster analysis identified three clusters with relevance for strength and balance control measures. For strength, in upright seated press peak anterior pushing force without backrest (effect size = 0.77) and ratio of peak anterior pushing force without and with backrest (effect size = 0.72) were significant. For balance control measures, trunk range of motion in forward (effect size = 0.81) and backward (effect size = 0.75) perturbations also contributed. High correlations (- 0.76 < r < - 0.53) were found between strength and control measures. The new testing device, protocol, and the cluster analysis show promising results in assessing impairment of trunk strength and control to empower an evidence-based classification.
Vinitra Swamy, Paola Mejia Domenzain, Julian Thomas Blackwell, Isadora Alves de Salles
Martin Alois Rohrmeier, Johannes Hentschel
Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Gabriel Girard, Marco Pizzolato, Alonso Ramirez Manzanares, Juan Luis Villarreal Haro, Alessandro Daducci, Ying-Chia Lin, Sara Sedlar, Caio Seguin, Kenji Marshall, Yang Ji