Biology subjects

Gurchiek, R. D.

Publications and source records attributed to Gurchiek, R. D..

3 recordsLinked to original sources

bioRxiv2025

Hamstrings muscle dynamics during the Nordic hamstring exercise and high-speed running

BackgroundThe Nordic hamstring exercise (NHE) and high-speed running are widely used training modalities to prevent hamstring strain injuries, yet the differences in the muscle lengths, forces, work, and power between these training modalities remain unclear. This study thus compared the dynamics of the most injured hamstrings muscle, biceps femoris long head (BFLH), for 14 participants (8 male and 6 female) performing the NHE and running between 4 and 8 m/s. MethodsWe used motion capture experiments and musculoskeletal simulation to quantify muscle fiber lengths and velocities, and muscle force, work, and power during the NHE and running. ResultsOur results show that peak muscle forces are greater during high-speed running (7.5 to 8 m/s) than the NHE, and that high-speed running also features longer muscle fiber lengths and higher muscle fiber lengthening velocities (p < 0.05). Negative muscle work was significantly greater during the NHE compared to running at all speeds (p < 0.001) because of the greater change in muscle fiber lengths during the NHE (p < 0.001). In contrast, peak negative muscle power was significantly lower during the NHE compared to running at 5 m/s and above (p < 0.01). ConclusionOur analysis reveals dramatic differences in the biomechanical demands of the NHE and running on the hamstrings muscles. Our results suggest that the two training modalities together provide complementary biomechanical stimuli to promote favorable BFLH injury prevention adaptations.

bioengineering↗

bioRxiv2024

Hamstrings are stretched more and faster during accelerative running compared to speed-matched constant speed running

BackgroundHamstring strain injuries are associated with significant time away from sport and high reinjury rates. Recent evidence suggests that hamstring injuries often occur during accelerative running, but investigations of hamstring mechanics have primarily examined constant speed running on a treadmill. To help fill this gap in knowledge, this study compares hamstring lengths and lengthening velocities between accelerative running and constant speed overground running. MethodsWe recorded 2 synchronized videos of 10 participants (5 female, 5 male) during 6 accelerative running trials and 6 constant speed running trials. We used OpenCap (a markerless motion capture system) to estimate body segment kinematics for each trial and a 3-dimensional musculoskeletal model to compute peak length and step-average lengthening velocity of the biceps femoris (long head) muscle-tendon unit. To compare running conditions, we used linear mixed regression models with running speed (normalized by the subject-specific maximum) as the independent variable. ResultsAt running speeds below 75% of top speed accelerative running resulted in greater peak lengths than constant speed running. For example, the peak hamstring muscle-tendon length when a person accelerated from running at only 50% of top speed was equivalent to running at a constant 88% of top speed. Lengthening velocities were greater during accelerative running at all running speeds. Differences in hip flexion kinematics primarily drove the greater peak muscle-tendon lengths and lengthening velocities observed in accelerative running. ConclusionHamstrings are subjected to longer muscle-tendon lengths and faster lengthening velocities in accelerative running compared to constant speed running. This provides a biomechanical explanation for the observation that hamstring strain injuries often occur during acceleration. Our results suggest coaches who monitor exposure to high-risk circumstances (long lengths, fast lengthening velocities) should consider the accelerative nature of running in addition to running speed.

bioengineering↗

bioRxiv2021

Wearables-Only Analysis of Muscle and Joint Mechanics: An EMG-Driven Approach

Complex sensor arrays prohibit practical deployment of existing wearables-based algorithms for free-living analysis of muscle and joint mechanics. Machine learning techniques have been proposed as a potential solution, however, they are less interpretable and generalizable when compared to physics-based techniques. Herein, we propose a hybrid method utilizing inertial sensor- and electromyography (EMG)-driven simulation of muscle contraction to characterize knee joint and muscle mechanics during walking gait. Machine learning is used only to map a subset of measured muscle excitations to a full set thereby reducing the number of required sensors. We demonstrate the utility of the approach for estimating net knee flexion moment (KFM) as well as individual muscle moment and work during the stance phase of gait across nine unimpaired subjects. Across all subjects, KFM was estimated with 0.91 %BW*H RMSE and strong correlations (r = 0.87) compared to ground truth inverse dynamics analysis. Estimates of individual muscle moments were strongly correlated (r = 0.81-0.99) with a reference EMG-driven technique using optical motion capture and a full set of electrodes as were estimates of muscle work (r = 0.88-0.99). Implementation of the proposed technique in the current work included instrumenting only three muscles with surface electrodes (lateral and medial gastrocnemius and vastus medialis) and both the thigh and shank segments with inertial sensors. These sensor locations permit instrumentation of a knee brace/sleeve facilitating a practically deployable mechanism for monitoring muscle and joint mechanics with performance comparable to the current state-of-the-art.

bioengineering↗