The purpose of this study was to validate measures of vertical oscillation (VO) and ground contact time (GCT) derived from a commercially-available, torso-mounted accelerometer compared with single marker kinematics and kinetic ground reaction force (GRF) data. Twenty-two semi-elite runners ran on an instrumented treadmill while GRF data (1000 Hz) and three-dimensional kinematics (200 Hz) were collected for 60 s across 5 different running speeds ranging from 2.7 to 3.9 m/s. Measurement agreement was assessed by Bland-Altman plots with 95% limits of agreement and by concordance correlation coefficient (CCC). The accelerometer had excellent CCC agreement (> 0.97) with marker kinematics, but only moderate agreement, and overestimated measures between 16.27 mm to 17.56 mm compared with GRF VO measures. The GCT measures from the accelerometer had very good CCC agreement with GRF data, with less than 6 ms of mean bias at higher speeds. These results indicate a torso-mounted accelerometer provides valid and accurate measures of torso-segment VO, but both a marker placed on the torso and the accelerometer yield systematic overestimations of center of mass VO. Measures of GCT from the accelerometer are valid when compared with GRF data, particularly at faster running speeds.
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http://dx.doi.org/10.1123/jab.2015-0200 | DOI Listing |
Gait Posture
January 2025
Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE 68182, USA; Department of Surgery and Research Service, Nebraska-Western Iowa Veterans Affairs Medical Center, Omaha, NE 68105, USA. Electronic address:
Background: This study leverages Artificial Neural Networks (ANNs) to predict lower limb joint moments and electromyography (EMG) signals from Ground Reaction Forces (GRF), providing a novel perspective on human gait analysis. This approach aims to enhance the accessibility and affordability of biomechanical assessments using GRF data, thus eliminating the need for costly motion capture systems.
Research Question: Can ANNs use GRF data to accurately predict joint moments in the lower limbs and EMG signals?
Methods: We employed ANNs to analyze GRF data and to use them to predict joint moments (363-trials; 4-datasets) and EMG signals (63-trials; 2-datasets).
Nat Sci Sleep
January 2025
Department of Radiology, The First Affiliated Hospital of Hunan Normal University (Hunan Provincial People's Hospital), Changsha, Hunan, People's Republic of China.
Background: COVID-19 has led to reports of fatigue and sleep problems. Brain function changes underlying sleep problems (SP) post-COVID-19 are unclear.
Purpose: This study investigated SP-related brain functional connectivity (FC) alterations.
Appl Sci (Basel)
June 2024
Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE 68182, USA.
Understanding metabolic cost through biomechanical data, including ground reaction forces (GRFs) and joint moments, is vital for health, sports, and rehabilitation. The long stabilization time (2-5 min) of indirect calorimetry poses challenges in prolonged tests. This study investigated using artificial neural networks (ANNs) to predict metabolic costs from the GRF and joint moment time series.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Physics, The Hong Kong University of Science and Technology, Hong Kong, China.
Dissolution of CO in water followed by the subsequent hydrolysis reactions is of great importance to the global carbon cycle, and carbon capture and storage. Despite numerous previous studies, the reactions are still not fully understood at the atomistic scale. Here, we combined ab initio molecular dynamics (AIMD) simulations with Markov state models to elucidate the reaction mechanisms and kinetics of CO in supercritical water both in the bulk and nanoconfined states.
View Article and Find Full Text PDFAnn N Y Acad Sci
January 2025
Department of Infectious Diseases and Public Health, City University of Hong Kong, Kowloon, Hong Kong.
Among hornbill birds, the critically endangered helmeted hornbill (Rhinoplax vigil) is notable for its casque (a bulbous beak protrusion) being filled with trabeculae and fronted by a very thick keratin layer. Casque function is debated but appears central to aerial jousting, where birds (typically males) collide casques at high speeds in a mid-flight display that is audible for more than 100 m. We characterized the structural relationship between the skull and casque anatomy using X-ray microtomography and quantitative trabecular network analysis to examine how the casque sustains extreme impact.
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