Publications by authors named "Reed D Gurchiek"
Background: While Nordic hamstring exercise (NHE) training has been shown to reduce hamstring strains, the muscle-specific adaptations to NHE across the 4 hamstrings remain unclear. This study investigates architectural and microstructural adaptations of the biceps femoris short head (BFsh), biceps femoris long head (BFlh), semitendinosus (ST), and semimembranosus (SM) in response to an NHE intervention.
Methods: Eleven subjects completed 9 weeks of supervised NHE training followed by 3 weeks of detraining.
Duchenne muscular dystrophy (DMD) is a progressive neuromuscular disorder that impairs daily functioning and results in premature death. Current clinical assessments are widely used for characterizing functional impairment but have limitations due to their subjective and effort-based nature and because they only capture a snapshot of symptoms at a single point in time. Digital health technologies, such as wearable devices, allow continuous collection of movement and physiological data during daily life and could provide objective measures of the impact of DMD symptoms on daily functioning.
View Article and Find Full Text PDFMultiscale hamstring muscle adaptations following 9 weeks of eccentric training.
J Sport Health Sci
October 2024
Article Synopsis
- Eccentric training via Nordic hamstring exercises (NHE) is effective in preventing hamstring strains by promoting changes in muscle structure, specifically increasing muscle fascicle length and adding sarcomeres in series within the muscle fibers.
- In a study with 12 participants, after 9 weeks of NHE training, the biceps femoris long-head (BFlh) showed significant improvements, including a 19% and 33% increase in fascicle length in the central and distal regions, respectively, along with a 40% increase in knee flexion strength.
- Following a 3-week period of no training (detraining), muscle adaptations such as fascicle length
Article Synopsis
- Hamstring injuries are frequent in field sports, especially during accelerative running, yet previous studies mainly examined constant-speed running mechanics.
- Researchers analyzed hamstring lengths and velocities in 10 participants across both accelerative and constant-speed running trials, revealing that accelerative running leads to longer hamstring lengths and higher lengthening velocities, particularly at speeds below 75% of maximum.
- The findings suggest that coaches and sports medicine professionals should focus on the specific demands of accelerative running, as it creates conditions that could increase the risk of hamstring injuries.
Background: Hamstring 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.
View Article and Find Full Text PDFEffect of IMU location on estimation of vertical ground reaction force during jumping.
Front Bioeng Biotechnol
March 2023
Article Synopsis
- * Eight male participants performed countermovement jumps while IMUs were attached to their sacrum, back, and chest, with GRF measured on a force plate for comparison.
- * Results showed that IMUs on the sacrum provided the most accurate GRF estimates, with smaller differences from the force plate measurements and significant correlation at a p-value of less than 0.001.
Article Synopsis
- Falls are common among individuals with multiple sclerosis (PwMS), leading to health complications, and fluctuations in MS symptoms can make it hard to assess fall risk through standard biannual clinical visits.
- Recent advancements in remote monitoring using wearable sensors provide a promising method to better understand fall risk by analyzing daily activity data from PwMS in real-world environments.
- A new dataset was created from 38 PwMS, which includes walking data and assessments to explore how free-living walking bouts relate to fall risk; results show that longer walking bouts are more effective for distinguishing between fallers and non-fallers compared to shorter ones.
Article Synopsis
- Inertial measurement units (IMUs) allow for studying lower-limb movements outside of a lab setting, providing flexibility in research.
- The authors developed an error-state Kalman filter to accurately estimate joint angles, stride length, and step width using data from seven wearable IMUs, incorporating a new method to correct joint axis measurements.
- The technique was tested against optical motion capture with 20 subjects walking in various ways, showing joint angle differences under 5 degrees and stride measurements under 0.13 meters for most gaits, indicating strong potential for practical gait analysis.
Article Synopsis
- Wearable sensors are used to assess gait and balance impairment over long periods, but there's a need to determine the optimal duration for accurate data collection without causing excessive burden on patients.
- Previous studies on sensor wear duration focused on various movement variables but often overlooked important measures like postural sway, highlighting the need for standardized methodologies.
- A new three-level framework was proposed, suggesting that 2 to 3 days of monitoring may suffice for capturing variability, while longer durations could strengthen correlations with patient-reported outcomes, emphasizing the importance of observation frequency and measure variability.
Wearables-Only Analysis of Muscle and Joint Mechanics: An EMG-Driven Approach.
IEEE Trans Biomed Eng
February 2022
Article Synopsis
- Complex sensor arrays currently limit the deployment of wearable algorithms for analyzing muscle and joint mechanics in everyday life; this study proposes a hybrid method that combines machine learning with traditional simulation techniques to address this issue.
- The new approach uses fewer sensors by mapping a limited set of muscle excitations to a complete dataset, effectively estimating knee joint mechanics during movement.
- The results showed strong accuracy in estimating the net knee flexion moment and individual muscle actions, demonstrating the method's potential for practical use in a wearable device format without compromising on performance compared to existing techniques.
Article Synopsis
- Human lower-limb kinematics are essential for various areas like gait analysis, performance enhancement, and injury prevention.
- A new estimation method using an error-state Kalman filter and body-worn IMUs shows promising accuracy in measuring lower-limb movements during walking, validated through both simulation and experiments.
- Future research will aim to expand this approach to a more complex 7-body model, improving insights into human movement.
Article Synopsis
- Researchers are exploring ways to estimate ground reaction forces in runners outside of lab settings using wearable sensors, moving beyond traditional tools like treadmills and force plates.
- The paper introduces a new framework that uses a mathematical model of a runner to estimate vertical ground reaction force during running, employing advanced filtering techniques to refine these estimations.
- Validation tests on 14 runners showed that the estimates closely matched measurements from instrumented treadmills, suggesting this framework could offer real-time feedback to runners on their performance metrics.
Modeling Muscle Synergies as a Gaussian Process: Estimating Unmeasured Muscle Excitations using a Measured Subset.
Annu Int Conf IEEE Eng Med Biol Soc
July 2020
Article Synopsis
- Continuous observation of muscle activity can reveal how muscles and joints handle loads during everyday activities, but using surface electromyography (sEMG) sensors on all muscles is a significant limitation.
- The researchers developed a model using a Gaussian process to predict muscle excitations in muscles that weren't directly measured, relying on sEMG data from only four leg muscles.
- The technique showed promising results, accurately estimating the muscle activity of six additional leg muscles during walking, with strong correlation coefficients (0.74 to 0.87) and low error rates (mean absolute error under 3%).
A Gaussian Process Model of Muscle Synergy Functions for Estimating Unmeasured Muscle Excitations Using a Measured Subset.
IEEE Trans Neural Syst Rehabil Eng
November 2020
Article Synopsis
- Estimating muscle excitations using a smaller array of sensors can enhance remote patient monitoring and personalize rehabilitation efforts.
- The introduction of a muscle synergy function allows for better understanding of how groups of muscles work together, which is modeled using Gaussian process regression.
- The study shows that muscle excitations can be accurately estimated from select muscles, achieving low error rates and high variance accounted for, paving the way for improved monitoring in everyday activities.
Wearables and Deep Learning Classify Fall Risk From Gait in Multiple Sclerosis.
IEEE J Biomed Health Inform
May 2021
Falls are a significant problem for persons with multiple sclerosis (PwMS). Yet fall prevention interventions are not often prescribed until after a fall has been reported to a healthcare provider. While still nascent, objective fall risk assessments could help in prescribing preventative interventions.
View Article and Find Full Text PDFArticle Synopsis
- The study investigates whether foot measurements can predict vertical jump performance in both men and women, addressing a gap in existing research that has primarily focused on male subjects.
- Researchers measured anthropometric data from 21 men and 21 women before they performed maximal countermovement jumps, finding no significant correlations for men but several significant negative correlations for women, indicating that smaller foot and toe sizes might lead to higher jump performance.
- The findings suggest further research is needed into these unexpected results, as well as the importance of including diverse groups in studies and being cautious about applying results across different sexes.
Article Synopsis
- - The study investigates the accuracy of gait event detection using a thigh-worn accelerometer, highlighting its potential usefulness in tracking patients with gait and balance issues, while pointing out a lack of data on foot contact and foot off event accuracy.
- - Researchers compared gait measurements from the accelerometer with ground truth data from a pressure treadmill among 32 healthy subjects walking at different speeds, evaluating performance through various statistical methods.
- - Results indicated that the algorithm for estimating gait variables showed strong correlations with reference measurements, but there was noticeable error in estimating foot contact and foot off events, suggesting the need for careful interpretation in biomechanical analyses.
Kinematic and kinetic comparison between American and Japanese collegiate pitchers.
J Sci Med Sport
December 2020
Article Synopsis
- This study aimed to investigate the pitching biomechanics differences between American and Japanese collegiate baseball pitchers to enhance training practices.
- Data from 11 pitchers from each country was collected using 3D motion capture, revealing that American pitchers were generally heavier, taller, and threw faster than their Japanese counterparts.
- Key findings included greater shoulder rotation in American pitchers during the arm-cocking phase and increased knee flexion in Japanese pitchers at ball release, with higher peak kinetics observed in American pitchers that could lead to both improved ball velocity and potential injury risks.
Article Synopsis
- Wearable sensors can enhance patient monitoring by accurately assessing biomechanics like joint and muscle movements, but using many devices can complicate practical use.* -
- Regression techniques could simplify this by allowing fewer sensors to estimate biomechanical data, which is explored through a review of 46 studies on this topic.* -
- The findings suggest incorporating domain knowledge leads to better performance, but current models lack openness and validation on impaired populations, highlighting the need for future research on accessible and validated algorithms.*
Article Synopsis
- - The text discusses the role of wearable sensors in digital medicine, particularly for improving patient monitoring and enabling personalized interventions in real-time.
- - It highlights the lack of open-source algorithms for analyzing wearable sensor data, which hinders the advancement of digital medicine, especially in areas like gait analysis that can provide important clinical insights.
- - An open-source platform for automated gait analysis was developed and tested, revealing that asymmetry indices from patients recovering from ACL surgery correlate more strongly with recovery time than standard metrics, indicating its potential for assessing rehabilitation progress.
Article Synopsis
- Recent advancements in wearable sensor technologies have focused primarily on knee joints, leaving a gap in accurate algorithms for hip joint angle estimation.
- A new algorithm has been developed that improves sensor alignment and orientation, allowing it to function effectively without needing specific calibration motions.
- Comparative testing showed this algorithm is more accurate than previous methods, achieving lower errors in estimating hip joint angles and range of motion, suggesting it’s a strong option for remote monitoring of hip joint activity.
Next Steps in Wearable Technology and Community Ambulation in Multiple Sclerosis.
Curr Neurol Neurosci Rep
September 2019
Article Synopsis
- Walking issues are common in people with multiple sclerosis (PwMS), impacting their quality of life and typically measured using self-reports and clinical tests that may not capture real-world walking fully.
- Wearable sensors, which can objectively assess various aspects of walking and provide data from everyday life, are under-researched in PwMS compared to other populations like older adults.
- Current studies have begun to use wearable tech but have mostly focused on pace, with limited exploration of important gait characteristics like variability, asymmetry, and complexity during daily activities, indicating a need for further research in this area.*
Article Synopsis
- - This study explores a new automated method for assessing sprint performance using machine learning and data from accelerometers worn by athletes during sprints, aiming to estimate key sprint metrics (maximal velocity and time to reach that velocity).
- - Researchers recorded the sprint times of 28 subjects over three 40-meter sprints and trained a classifier to help identify the sprint start while also developing regression models to estimate performance parameters.
- - The automated method showed a small error margin in estimating sprint times compared to traditional methods, indicating the potential benefits of combining both approaches for improved performance assessment.
Physiological and Biomechanical Responses to an Acute Bout of High Kicking in Dancers.
J Strength Cond Res
October 2018
Article Synopsis
- A study investigated the physiological and biomechanical effects of high-kick routines on adolescent dancers, focusing on measures like blood lactate, heart rate, and jump performance.
- Results showed significant increases in blood lactate levels and decreases in jump height and peak power after the high-kick protocol, while heart rates peaked during the exercise.
- The findings suggest that high-kick dance is demanding on the body and may require additional strength and conditioning training to manage fatigue effectively.