Synchronization of multiple data collection systems is necessary for accurate temporal alignment of data, and is particularly important when considering rapid movements which occur in less than one second. This paper describes a novel method for synchronizing multiple data collection instruments including load cells and a motion capture system, using a common analog signal. An application of the synchronization method is demonstrated using biomechanical data collected during a rapid reach-to-grasp reaction, where data from motion capture and load cells are collected. Results are provided to validate and demonstrate the accuracy of the synchronization of motion capture with other data collection systems. During the reach-to-grasp trials, delays between the data collection systems ranged from 4ms to 235ms. The large range and variability in delay times between trials highlights the need for synchronization on a continual basis, rather than application of an average or constant value to correct for time delays between systems.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.gaitpost.2016.10.002 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A reproducible representation of healthy tibiofemoral kinematics during stair descent using REFRAME - part I: REFRAME foundations and validation.
Sci Rep
January 2025
Research and Development, Aesculap AG, Tuttlingen, Germany.
In clinical movement biomechanics, kinematic measurements are collected to characterise the motion of articulating joints and investigate how different factors influence movement patterns. Representative time-series signals are calculated to encapsulate (complex and multidimensional) kinematic datasets succinctly. Exacerbated by numerous difficulties to consistently define joint coordinate frames, the influence of local frame orientation and position on the characteristics of the resultant kinematic signals has been previously proven to be a major limitation.
View Article and Find Full Text PDFDeepLabCut custom-trained model and the refinement function for gait analysis.
Sci Rep
January 2025
Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.
The current gold standard for the study of human movement is the marker-based motion capture system that offers high precision but constrained by costs and controlled environments. Markerless pose estimation systems emerge as ecological alternatives, allowing unobtrusive data acquisition in natural settings. This study compares the performance of two popular markerless systems, OpenPose (OP) and DeepLabCut (DLC), in assessing locomotion.
View Article and Find Full Text PDFMultidimensional free shape-morphing flexible neuromorphic devices with regulation at arbitrary points.
Nat Commun
January 2025
Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, National Institute for Advanced Materials, Nankai University, Tianjin, China.
Biological neural systems seamlessly integrate perception and action, a feat not efficiently replicated in current physically separated designs of neural-imitating electronics. This segregation hinders coordination and functionality within the neuromorphic system. Here, we present a flexible device tailored for neuromorphic computation and muscle actuation.
View Article and Find Full Text PDFConcurrent validity of skin-based motion capture systems in measuring dynamic lumbar intervertebral angles.
J Biomech
January 2025
Faculty of Medicine, Memorial University of Newfoundland, St. John's, Canada. Electronic address:
Spine kinematics are commonly measured by external sensors such as motion capture and accelerometers. However, these skin-based measures cannot directly capture intervertebral motion of the lumbar spine. To date, research in this area has focused on the estimation of intervertebral kinematics using static trials but no study has analyzed agreement throughout the dynamic range of motion.
View Article and Find Full Text PDFDataset of running kinematics, kinetics and muscle activation at different speeds, surface gradients, cadences and with forward trunk lean.
Data Brief
June 2024
NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Department of Nutrition and Movement Sciences, Maastricht, the Netherlands.
Data Collection Process: This dataset includes running biomechanics measured using an instrumented treadmill combined with three- dimensional motion capture and surface muscle activation among 19 healthy participants (10 males, 9 females, mean ± SD age 23.6 ± 3.7 years, body height 174.
View Article and Find Full Text PDFWant 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!