In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the direction of the (2) earth frame-based estimate. However, in sports situations that are characterized by relatively large linear accelerations and/or close magnetic sources, such as wheelchair sports, obtaining accurate IMU orientation estimates is challenging. In these situations, applying the MW filter in the regular way, i.e., with the same magnitude of correction at all time frames, may lead to estimation errors. Therefore, in this study, the MW filter was extended with machine learning to distinguish instances in which a small correction magnitude is beneficial from instances in which a large correction magnitude is beneficial, to eventually arrive at accurate body segment orientations in IMU-challenging sports situations. A machine learning algorithm was trained to make this distinction based on raw IMU data. Experiments on wheelchair sports were performed to assess the validity of the extended MW filter, and to compare the extended MW filter with the original MW filter based on comparisons with a motion capture-based reference system. Results indicate that the extended MW filter performs better than the original MW filter in assessing instantaneous trunk inclination (7.6 vs. 11.7◦ root-mean-squared error, RMSE), especially during the dynamic, IMU-challenging situations with moving athlete and wheelchair. Improvements of up to 45% RMSE were obtained for the extended MW filter compared with the original MW filter. To conclude, the machine learning-based extended MW filter has an acceptable accuracy and performs better than the original MW filter for the assessment of body segment orientation in IMU-challenging sports situations.
Fields neighboring the disciplines of kinesiology and sports science have called for more interdisciplinary work, including the adoption of critical approaches to research. This scoping review explored the degree to which critically-aligned research has developed within these disciplines. The goal was to identify who this research studied, what methods were used, and which theoretical and conceptual frameworks were adopted. Publications between 2010-2022 in six top kinesiology and sports science journals using four databases were searched using keywords to identify critically-aligned research. A multi-step screening process was used to identify and sort articles that adequately fit the criteria of critically-aligned research. The scoping review identified 5666 entries of which 3300 were unique publications. 76 articles were assessed to be critically-aligned. Four themes regarding demographics emerged: Geographic area, gender, race/ethnicity/indigeneity, and inequality/inequity. Regarding methodology, three major theoretical and conceptual frameworks emerged: ecological, socio-economic, and cultural. Overall, a relatively small number of studies fit our search criteria, suggesting that critically-aligned research remains at the margins of the disciplines. For the studies that were critically-aligned, they often centered the Global North and were inconsistent in their application of categories such as race, ethnicity, inequality and equity. These studies were diverse in their methodological approach while relying on ecological, socio-economic, and cultural frameworks. To heed the calls for a more interdisciplinary approach, and to advance the disciplines more generally, kinesiology and sports science should expand their adoption of critical approaches to research.
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This report is an introduction in into the topic of sustainability and the world of sports. It contains literature studies and several empirical studies. It provides overviews of current academic and professional sources and suggests further research.
