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Treadmills are essential to the study of human and animal locomotion as well as for applied diagnostics in both sports and medicine. The quantification of relevant biomechanical and physiological variables requires a precise regulation of treadmill belt velocity (TBV). Here, we present a novel method for time-efficient tracking of TBV using standard 3D motion capture technology. Further, we analyzed TBV fluctuations of four different treadmills as seven participants walked and ran at target speeds ranging from 1.0 to 4.5 m/s. Using the novel method, we show that TBV regulation differs between treadmill types, and that certain features of TBV regulation are affected by the subjects’ body mass and their locomotion speed. With higher body mass, the TBV reductions in the braking phase of stance became higher, even though this relationship differed between locomotion speeds and treadmill type (significant body mass × speed × treadmill type interaction). Average belt speeds varied between about 98 and 103% of the target speed. For three of the four treadmills, TBV reduction during the stance phase of running was more intense (> 5% target speed) and occurred earlier (before 50% of stance phase) unlike the typical overground center of mass velocity patterns reported in the literature. Overall, the results of this study emphasize the importance of monitoring TBV during locomotor research and applied diagnostics. We provide a novel method that is freely accessible on Matlab’s file exchange server (“getBeltVelocity.m”) allowing TBV tracking to become standard practice in locomotion research.
Effect of downhill running on biomechanical risk factors associated with iliotibial band syndrome
(2022)
The purpose of this study was to identify the influence of downhill running on biomechanical risk factors for iliotibial band syndrome. We conducted a 3D motion analysis of 22 females and males running on an instrumented treadmill at four different inclinations (0%, -5%, -10%, -15%) at a speed of 3.5 m/s. We found significant differences for biomechanical risk factors associated with iliotibial band syndrome. Peak knee flexion angle at initial ground contact (p < .001), peak knee adduction angle (p = .005), and iliotibial band strain (p < .001) systematically increased with increasing slope. Downhill running increases biomechanical risk factors for iliotibial band syndrome. Our results highlight the need to consider the individual running environment in assessing overuse injury risk in runners.
This study aimed to compare a simplified calculation of the knee abduction moment with the traditional inverse dynamics calculation when athletes perform fake-cut maneuvers with different complexities. In the simplified calculation, we multiply the force vector with its lever arm to the knee, projected onto the local coordinate system of the proximal thigh, hence neglecting the inertial contributions from distal segments. We found very strong ranking consistency using Spearman’s rank correlation coefficient when using the simplified method compared to the traditional calculation. Independent of the tasks, the simplified method resulted in higher moments than the inverse dynamics. This was caused by ignoring the moment caused by segment linear acceleration generating a counteracting moment by about 7%. An alternative to the complex calculations of inverse dynamics can be used to investigate the contributions of the GRF magnitude and its lever arm to the knee.
Non-contact anterior cruciate ligament injuries typically occur during cutting maneuvers and are associated with high peak knee abduction moments (KAM) within early stance. To screen athletes for injury risk or quantify the efficacy of prevention programs, it may be necessary to design tasks that mimic game situations. Thus, this study compared KAMs and ranking consistency of female handball players in three sport-specific fake-and-cut tasks of increasing complexity. The biomechanics of female handball players (n = 51, mean ± SD: 66.9 ± 7.8 kg, 1.74 ± 0.06 m, 19.2 ± 3.4 years) were recorded with a 3D motion capture system and force plates during three standardized fake-and-cut tasks. Task 1 was designed as a simple pre-planned cut, task 2 included catching a ball before a pre-planned cut in front of a static defender, and task 3 was designed as an unanticipated cut with three dynamic defenders involved. Inverse dynamics were used to calculate peak KAM within the first 100 ms of stance. KAM was decomposed into the frontal plane knee joint moment arm and resultant ground reaction force. RANOVAs (α ≤ 0.05) were used to reveal differences in the KAM magnitudes, moment arm, and resultant ground reaction force for the three tasks. Spearman's rank correlations were calculated to test the ranking consistency of the athletes' KAMs. There was a significant task main effect on KAM (p = 0.02; ηp2 = 0.13). The KAM in the two complex tasks was significantly higher (task 2: 1.73 Nm/kg; task 3: 1.64 Nm/kg) than the KAM in the simplest task (task 1: 1.52 Nm/kg). The ranking of the peak KAM was consistent regardless of the task complexity. Comparing tasks 1 and 2, an increase in KAM resulted from an increased frontal plane moment arm. Comparing tasks 1 and 3, higher KAM in task 3 resulted from an interplay between both moment arm and the resultant ground reaction force. In contrast to previous studies, unanticipated cutting maneuvers did not produce the highest KAMs. These findings indicate that the players have developed an automated sport-specific cutting technique that is utilized in both pre-planned and unanticipated fake-and-cut tasks.