A Pilot Study in Sensor Instrumented Training (SIT) - Ground Contact Time for Monitoring Fatigue and Curve Running Technique
Author:
Blauberger P.1, Fukushima T.1, Russomanno T. G.12, Lames M.1
Affiliation:
1. Chair of Performance Analysis and Sports Informatics , Technical University of Munich , Munich , Germany 2. Faculty of Physical Education , University of Brasília , Brasília , Brazil
Abstract
Abstract
This study examines the possibilities of sensor-instrumented training (SIT) in mid-distance running training sessions. Within this framework, variations of ground contact time (GCT) between straight and curved running, as well as GCT as a fatigue indicator, are explored. Seven experienced runners, with two elite female athletes, participated in two training protocols: 15 sets of 400 m with 1-minute rest and five sets of 300 m with 3-minute rest. GCT was calculated using two inertial measurement units (IMU) attached to the athletes’ feet. The running speed of all athletes was measured with wearable GPS devices. GCT showed variations between inner and outer feet, notably during curve running (300m: 2.56%; 400m: 2.35%). However, for the 300m runs, statistically insignificant GCT differences were more pronounced in straight runs (3.54%) than in curve runs (2.56%), contrasting with the typical assumption of higher differences in curve running. A fatigue-indicating pattern is visible in GCT, as well as speed curves. Other data of this study are consistent with prior research that has observed differences between the inner and outer foot during curve running, while our understanding of the development throughout the training session is enhanced. Using SIT can be a valuable tool for refining curve running technique. By incorporating novel sensing technology, the possibilities enhance our understanding of running kinematics and offer an excellent application of SIT in sports.
Publisher
Walter de Gruyter GmbH
Reference21 articles.
1. Alt, T., Heinrich, K., Funken, J., & Potthast, W. (2015). Lower extremity kinematics of athletics curve sprinting. Journal of Sports Sciences, 33(6), 552 560. https://doi.org/10.1080/02640414.2014.960881 2. Apte, S., Prigent, G., Stöggl, T., Martínez, A., Snyder, C., Gremeaux-Bader, V., & Aminian, K. (2021). Biomechanical Response of the Lower Extremity to Running-Induced Acute Fatigue: A Systematic Review. Frontiers in Physiology, 12, 646042. https://doi.org/10.3389/fphys.2021.646042 3. Baca, A., Dabnichki, P., Hu, C.-W., Komfeind, P., & Exel, J. (2022). Ubiquitous Computing in Sports and Physical Activity-Recent Trends and Developments. Sensors (Basel, Switzerland), 22(21). https://doi.org/10.3390/s22218370 4. Blauberger, P., Horsch, A., & Lames, M. (2021). Detection of Ground Contact Times with Inertial Sensors in Elite 100-m Sprints under Competitive Field Conditions. Sensors (Basel, Switzerland), 21(21). https://doi.org/10.3390/s21217331 5. Churchill, S. M., Salo, A. I. T., & Trewartha, G. (2015). The effect of the bend on technique and performance during maximal effort sprinting. Sports Biomechanics, 14(1), 106 121. https://doi.org/10.1080/14763141.2015.1024717
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