Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data [PDF]
Bioengineering, 2023 Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into muscle and joint loading at an in vivo level, aiding clinical decision-making.
Abhishek Dasgupta +3 more
doaj +7 more sources
Gap Reconstruction in Optical Motion Capture Sequences Using Neural Networks [PDF]
Sensors, 2021 Optical motion capture is a mature contemporary technique for the acquisition of motion data; alas, it is non-error-free. Due to technical limitations and occlusions of markers, gaps might occur in such recordings.
Przemysław Skurowski, Magdalena Pawlyta
doaj +6 more sources
Detection and Classification of Artifact Distortions in Optical Motion Capture Sequences [PDF]
Sensors, 2022 Optical motion capture systems are prone to errors connected to marker recognition (e.g., occlusion, leaving the scene, or mislabeling). These errors are then corrected in the software, but the process is not perfect, resulting in artifact distortions ...
Przemysław Skurowski, Magdalena Pawlyta
doaj +5 more sources
On the Noise Complexity in an Optical Motion Capture Facility [PDF]
Sensors, 2019 Optical motion capture systems are state-of-the-art in motion acquisition; however, like any measurement system they are not error-free: noise is their intrinsic feature.
Przemysław Skurowski, Magdalena Pawlyta
doaj +6 more sources
Performance Evaluation of Optical Motion Capture Sensors for Assembly Motion Capturing [PDF]
IEEE Access, 2021 The optical motion capture (MoCap) sensor provides an effective way to capture human motions and transform them into valuable data that can be applied to certain tasks, e.g. robot learning from demonstration (LfD).
Haopeng Hu +4 more
doaj +3 more sources
Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis [PDF]
Sensors, 2021 Optical motion capture is currently the most popular method for acquiring motion data in biomechanical applications. However, it presents a number of problems that make the process difficult and inefficient, such as marker occlusions and unwanted ...
Javier Cuadrado +3 more
doaj +3 more sources
Optical motion capture accuracy is task-dependent in assessing wrist motion. [PDF]
J Biomech, 2021 Optical motion capture (OMC) systems are commonly used to capture in-vivo three-dimensional joint kinematics. However, the skin-based markers may not reflect the underlying bone movement, a source of error known as soft tissue artifact (STA).
McHugh B +4 more
europepmc +4 more sources
Metrological Evaluation of Human–Robot Collaborative Environments Based on Optical Motion Capture Systems [PDF]
Sensors, 2021 In the context of human–robot collaborative shared environments, there has been an increase in the use of optical motion capture (OMC) systems for human motion tracking.
Leticia González +3 more
doaj +3 more sources
Gait Recognition Using Optical Motion Capture: A Decision Fusion Based Method [PDF]
Sensors, 2021 Human identification based on motion capture data has received signification attentions for its wide applications in authentication and surveillance systems.
Li Wang +3 more
doaj +3 more sources
Sensor Fusion for Enhancing Motion Capture: Integrating Optical and Inertial Motion Capture Systems
SensorsThis study aimed to create and evaluate an optimization-based sensor fusion algorithm that combines Optical Motion Capture (OMC) and Inertial Motion Capture (IMC) measurements to provide a more efficient and reliable gap-filling process for OMC ...
Hailey N. Hicks +2 more
doaj +2 more sources