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.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2870720PMC
http://dx.doi.org/10.1007/s10439-009-9865-0DOI Listing

Publication Analysis

Top Keywords

motion loading
16
loading system
8
biological response
8
motion
6
loading
6
jmls
6
development validation
4
validation motion
4
system rat
4
rat knee
4

Similar Publications

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.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

Wearable System Applications in Performance Analysis of RaceRunning Athletes with Disabilities.

Sensors (Basel)

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.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!