Lower extremity joint kinetics and lumbar curvature during squat and stoop lifting

<p>Abstract</p> <p>Background</p> <p>In this study, kinematics and kinetics of the lower extremity joint and the lumbar lordosis during two different symmetrical lifting techniques(squat and stoop) were examined using the three-dimensional motion analysis.</p> <p>Methods</p> <p>Twenty-six young male volunteers were selected for the subjects in this study. While they lifted boxes weighing 5, 10 and 15 kg by both squat and stoop lifting techniques, their motions were captured and analyzed using the 3D motion analysis system which was synchronized with two forceplates and the electromyographic system. Joint kinematics was determined by the forty-three reflective markers which were attached on the anatomical locations based on the VICON Plug-in-Gait marker placement protocol. Joint kinetics was analyzed by using the inverse dynamics. Paired t-test and Kruskal-Wallis test was used to compare the differences of variables between two techniques, and among three different weights. Correlation coefficient was calculated to explain the role of lower limb joint motion in relation to the lumbar lordosis.</p> <p>Results</p> <p>There were not significant differences in maximum lumbar joint moments between two techniques. The hip and ankle contributed the most part of the support moment during squat lifting, and the knee flexion moment played an important role in stoop lifting. The hip, ankle and lumbar joints generated power and only the knee joint absorbed power in the squat lifting. The knee and ankle joints absorbed power, the hip and lumbar joints generated power in the stoop lifting. The bi-articular antagonist muscles' co-contraction around the knee joint during the squat lifting and the eccentric co-contraction of the gastrocnemius and the biceps femoris were found important for maintaining the straight leg during the stoop lifting. At the time of lordotic curvature appearance in the squat lifting, there were significant correlations in all three lower extremity joint moments with the lumbar joint. Differently, only the hip moment had significant correlation with the lumbar joint in the stoop lifting.</p> <p>Conclusion</p> <p>In conclusion, the knee extension which is prominent kinematics during the squat lifting was produced by the contributions of the kinetic factors from the hip and ankle joints(extensor moment and power generation) and the lumbar extension which is prominent kinematics during the stoop lifting could be produced by the contributions of the knee joint kinetic factors(flexor moment, power absorption, bi-articular muscle function).</p

Lower extremity joint kinetics and lumbar curvature during squat and stoop lifting

<p>Abstract</p> <p>Background</p> <p>In this study, kinematics and kinetics of the lower extremity joint and the lumbar lordosis during two different symmetrical lifting techniques(squat and stoop) were examined using the three-dimensional motion analysis.</p> <p>Methods</p> <p>Twenty-six young male volunteers were selected for the subjects in this study. While they lifted boxes weighing 5, 10 and 15 kg by both squat and stoop lifting techniques, their motions were captured and analyzed using the 3D motion analysis system which was synchronized with two forceplates and the electromyographic system. Joint kinematics was determined by the forty-three reflective markers which were attached on the anatomical locations based on the VICON Plug-in-Gait marker placement protocol. Joint kinetics was analyzed by using the inverse dynamics. Paired t-test and Kruskal-Wallis test was used to compare the differences of variables between two techniques, and among three different weights. Correlation coefficient was calculated to explain the role of lower limb joint motion in relation to the lumbar lordosis.</p> <p>Results</p> <p>There were not significant differences in maximum lumbar joint moments between two techniques. The hip and ankle contributed the most part of the support moment during squat lifting, and the knee flexion moment played an important role in stoop lifting. The hip, ankle and lumbar joints generated power and only the knee joint absorbed power in the squat lifting. The knee and ankle joints absorbed power, the hip and lumbar joints generated power in the stoop lifting. The bi-articular antagonist muscles' co-contraction around the knee joint during the squat lifting and the eccentric co-contraction of the gastrocnemius and the biceps femoris were found important for maintaining the straight leg during the stoop lifting. At the time of lordotic curvature appearance in the squat lifting, there were significant correlations in all three lower extremity joint moments with the lumbar joint. Differently, only the hip moment had significant correlation with the lumbar joint in the stoop lifting.</p> <p>Conclusion</p> <p>In conclusion, the knee extension which is prominent kinematics during the squat lifting was produced by the contributions of the kinetic factors from the hip and ankle joints(extensor moment and power generation) and the lumbar extension which is prominent kinematics during the stoop lifting could be produced by the contributions of the knee joint kinetic factors(flexor moment, power absorption, bi-articular muscle function).</p

Risk factors for the onset and persistence of neck pain in undergraduate students: 1-year prospective cohort study

<p>Abstract</p> <p>Background</p> <p>Although neck pain is common in young adulthood, studies on predictive factors for its onset and persistence are scarce. It is therefore important to identify possible risk factors among young adults so as to prevent the development of neck pain later in life.</p> <p>Methods</p> <p>A prospective study was carried out in healthy undergraduate students. At baseline, a self-administered questionnaire and standardized physical examination were used to collect data on biopsychosocial factors. At 3, 6, 9, and 12 months thereafter, follow-up data were collected on the incidence of neck pain. Those who reported neck pain on ≥ 2 consecutive follow-ups were categorized as having persistent neck pain. Two regression models were built to analyze risk factors for the onset and persistence of neck pain.</p> <p>Results</p> <p>Among the recruited sample of 684 students, 46% reported the onset of neck pain between baseline and 1-year follow-up, of whom 33% reported persistent neck pain. The onset of neck pain was associated with computer screen position not being level with the eyes and mouse position being self-rated as suitable. Factors that predicted persistence of neck pain were position of the keyboard being too high, use of computer for entertainment < 70% of total computer usage time, and students being in the second year of their studies.</p> <p>Conclusion</p> <p>Neck pain is quite common among undergraduate students. This study found very few proposed risk factors that predicted onset and persistence of neck pain. The future health of undergraduate students deserves consideration. However, there is still much uncertainty about factors leading to neck pain and more research is needed on this topic.</p

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io