Whole-body predictors of wrist shot accuracy in ice hockey: a kinematic analysis.

The purpose of this study was to identify joint angular kinematics that corresponds to shooting accuracy in the stationary ice hockey wrist shot. Twenty-four subjects participated in this study, each performing 10 successful shots on four shooting targets. An eight-camera infra-red motion capture system (240 Hz), along with passive reflective markers, was used to record motion of the joints, hockey stick, and puck throughout the performance of the wrist shot. A multiple regression analysis was carried out to examine whole-body kinematic variables with accuracy scores as the dependent variable. Significant accuracy predictors were identified in the lower limbs, torso and upper limbs. Interpretation of the kinematics suggests that characteristics such as a better stability of the base of support, momentum cancellation, proper trunk orientation and a more dynamic control of the lead arm throughout the wrist shot movement are presented as predictors for the accuracy outcome. These findings are substantial as they not only provide a framework for further analysis of motor control strategies using tools for accurate projection of objects, but more tangibly they may provide a comprehensive evidence-based guide to coaches and athletes for planned training to improve performance.

biomechZoo: An open-source toolbox for the processing, analysis, and visualization of biomechanical movement data.

It is common for biomechanics data sets to contain numerous dependent variables recorded over time, for many subjects, groups, and/or conditions. These data often require standard sorting, processing, and analysis operations to be performed in order to answer research questions. Visualization of these data is also crucial. This manuscript presents biomechZoo, an open-source toolbox that provides tools and graphical user interfaces to help users achieve these goals. The aims of this manuscript are to (1) introduce the main features of the toolbox, including a virtual three-dimensional environment to animate motion data (Director), a data plotting suite (Ensembler), and functions for the computation of three-dimensional lower-limb joint angles, moments, and power and (2) compare these computations to those of an existing validated system. To these ends, the steps required to process and analyze a sample data set via the toolbox are outlined. The data set comprises three-dimensional marker, ground reaction force (GRF), joint kinematic, and joint kinetic data of subjects performing straight walking and 90° turning manoeuvres. Joint kinematics and kinetics processed within the toolbox were found to be similar to outputs from a commercial system. The biomechZoo toolbox represents the work of several years and multiple contributors to provide a flexible platform to examine time-series data sets typical in the movement sciences. The toolbox has previously been used to process and analyse walking, running, and ice hockey data sets, and can integrate existing routines, such as the KineMat toolbox, for additional analyses. The toolbox can help researchers and clinicians new to programming or biomechanics to process and analyze their data through a customizable workflow, while advanced users are encouraged to contribute additional functionality to the project. Students may benefit from using biomechZoo as a learning and research tool. It is hoped that the toolbox can play a role in advancing research in the movement sciences. The biomechZoo m-files, sample data, and help repositories are available online (http://www.biomechzoo.com) under the Apache 2.0 License. The toolbox is supported for Matlab (r2014b or newer, The Mathworks Inc., Natick, USA) for Windows (Microsoft Corp., Redmond, USA) and Mac OS (Apple Inc., Cupertino, USA).