The influence of biomechanical stimuli on modulating cartilage homeostasis is well recognized. However, many aspects of cellular mechanotransduction in cartilage remain unknown. We developed a computer-controlled joint motion and loading system (JMLS) to study the biological response of cartilage under well-characterized mechanical loading environments. The JMLS was capable of controlling (i) angular displacement, (ii) motion frequency, (iii) magnitude of the axial compressive load applied to the moving joint, and it featured real-time monitoring. The accuracy and repeatability of angular position measurements, the kinematic misalignment error as well as the repositioning error of the JMLS were evaluated. The effectiveness of the JMLS in implementing well-defined loading protocols such as moderate Passive Motion Loading (PML) and increased Compressive Motion Loading (CML) were tested. The JMLS demonstrated remarkable accuracy and reliability for the measurement and kinematics tests. Moreover, the effectiveness test demonstrated the ability of the JMLS to produce an effective stimulus via PML that led to the suppression of the catabolic effects of immobilization. Interestingly, the biological response of the CML group was catabolic and exhibited a pattern similar to that observed in the immobilization group. This novel non-invasive system may be useful for joint biomechanics studies that require different treatment conditions of load and motion in vivo.
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http://dx.doi.org/10.1007/s10439-009-9865-0 | DOI Listing |
Sensors (Basel)
December 2024
Department of Engineering and Industrial Design, Magdeburg-Stendal University of Applied Sciences, 39110 Magdeburg, Germany.
Inappropriate, excessive, or overly strenuous training of sport horses can result in long-term injury, including the premature cessation of a horse's sporting career. As a countermeasure, this study demonstrates the easy implementation of a biomechanical load monitoring system consisting of five commercial, multi-purpose inertial sensor units non-invasively attached to the horse's distal limbs and trunk. From the data obtained, specific parameters for evaluating gait and limb loads are derived, providing the basis for objective exercise load management and successful injury prevention.
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December 2024
Faculty of Medicine, Department of Kinesiology, Université Laval, Quebec City, QC G1V OA6, Canada.
Foot strike patterns influence vertical loading rates during running. Running retraining interventions often include switching to a new foot strike pattern. Sudden changes in the foot strike pattern may be uncomfortable and may lead to higher step-to-step variability.
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December 2024
Federal Highway Research Institute, 51427 Bergisch Gladbach, Germany.
Weigh-in-motion (WIM) systems aim to estimate a vehicle's weight by measuring static wheel loads as it passes at highway speed over roadway-embedded sensors. Vehicle oscillations and the resulting dynamic load components are critical factors affecting measurements and limiting accuracy. As of now, a satisfactory solution has yet to be found.
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December 2024
School of Sport and Physical Activity, College of Health, Wellbeing and Life Sciences, Sheffield Hallam University, Sheffield S10 2BP, UK.
RaceRunning is a sport for disabled people and successful performance depends on reducing the amount of time spent travelling a specific distance. Performance analysis in RaceRunning athletes is based on traditional methods such as recording race time, distances travelled and frequency (sets and reps) that are not sufficient for monitoring training loads. The aims of this study were to monitor training loads in typical training sessions and evaluate technical adaptations in RaceRunning performance by acquiring sensor metrics.
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December 2024
Applied Biomechanics and Sport Technology Research Group, Autonomous University of Madrid, 28049 Madrid, Spain.
The countermovement jump (CMJ) is a widely used test to assess lower body neuromuscular performance. This study aims to analyze the validity and reliability of an iOS application using artificial intelligence to measure CMJ height, force, velocity, and power in unloaded and loaded conditions. Twelve physically active participants performed 12 CMJs with external loads ranging from 0% to 70% of their body mass while being simultaneously monitored with a pair of force platforms and the My Jump Lab application.
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