scholarly journals Gap Reconstruction in Optical Motion Capture Sequences Using Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6115
Author(s):  
Przemysław Skurowski ◽  
Magdalena Pawlyta
Keyword(s):  
Neural Networks ◽  
Motion Capture ◽  
Matrix Completion ◽  
Input Sequence ◽  
Feedforward Neural Networks ◽  
Network Architectures ◽  
Motion Data ◽  
Optical Motion ◽  
Body Kinematic ◽  
Optical Motion Capture

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. The article reviews various neural network architectures applied to the gap-filling problem in motion capture sequences within the FBM framework providing a representation of body kinematic structure. The results are compared with interpolation and matrix completion methods. We found out that, for longer sequences, simple linear feedforward neural networks can outperform the other, sophisticated architectures, but these outcomes might be affected by the small amount of data availabe for training. We were also able to identify that the acceleration and monotonicity of input sequence are the parameters that have a notable impact on the obtained results.

Research on Intangible Cultural Heritage Based on Motion Capture

2014 ◽  
Vol 568-570 ◽  
pp. 676-680
Author(s):  
Si Xi Chen ◽  
Shu Chen
Keyword(s):  
Cultural Heritage ◽  
Motion Capture ◽  
Low Cost ◽  
Three Dimensional ◽  
Intangible Cultural Heritage ◽  
Three Dimensional Model ◽  
Motion Data ◽  
Optical Motion ◽  
Capture Method ◽  
Optical Motion Capture

The application of digital technology on the protection of intangible cultural heritage is a major topic of research in recent years. The motion capture technology of protection will gradually replace the traditional recording methods such as texts, pictures and videos. It is valuable to build a high-fidelity, high-modular and low-cost digital platform for choreographic data collection and extended application. This paper studies the intangible cultural heritage of Quanzhou breast-clapping dance, one of the most famous choreographic intangible cultural heritages from China with standard optical motion capture method. The data are acquiring and processing after the dance motion capture, we binds the motion data and three-dimensional model using Motion Builder and build digital demonstration platform base on an OGRE engine to display the movements. The viewer can view at any angle and distance. The system can be easily applied in motion intangible cultural heritages protection project. Furthermore, the system can be provided versatile motion data for additional use.

Dynamic Joint Motions in Occupational Environments as Indicators of Potential Musculoskeletal Injury Risk

2020 ◽  
pp. 1-8
Author(s):  
Jonathan S. Dufour ◽  
Alexander M. Aurand ◽  
Eric B. Weston ◽  
Christopher N. Haritos ◽  
Reid A. Souchereau ◽  
...  
Keyword(s):  
Motion Capture ◽  
Task Difficulty ◽  
Injury Risk ◽  
Occupational Injury ◽  
Measurement Unit ◽  
Motion Capture System ◽  
Motion Data ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Joint Motions

The objective of this study was to test the feasibility of using a pair of wearable inertial measurement unit (IMU) sensors to accurately capture dynamic joint motion data during simulated occupational conditions. Eleven subjects (5 males and 6 females) performed repetitive neck, low-back, and shoulder motions simulating low- and high-difficulty occupational tasks in a laboratory setting. Kinematics for each of the 3 joints were measured via IMU sensors in addition to a “gold standard” passive marker optical motion capture system. The IMU accuracy was benchmarked relative to the optical motion capture system, and IMU sensitivity to low- and high-difficulty tasks was evaluated. The accuracy of the IMU sensors was found to be very good on average, but significant positional drift was observed in some trials. In addition, IMU measurements were shown to be sensitive to differences in task difficulty in all 3 joints (P < .05). These results demonstrate the feasibility for using wearable IMU sensors to capture kinematic exposures as potential indicators of occupational injury risk. Velocities and accelerations demonstrate the most potential for developing risk metrics since they are sensitive to task difficulty and less sensitive to drift than rotational position measurements.

scholarly journals Use of Human Motion Data to Train Wearable Robots

2021 ◽  
Vol 12 (6) ◽  
pp. 807-811
Author(s):  
JIBUM JUNG Et.al
Keyword(s):  
Motion Capture ◽  
Human Motion ◽  
Machine Learning Algorithms ◽  
Walking Robots ◽  
Motion Capture System ◽  
Motion Data ◽  
Wearable Robots ◽  
Human Movements ◽  
Optical Motion ◽  
Optical Motion Capture

Development of wearable robots is accelerating. Walking robots mimic human behavior and must operate without accidents. Human motion data are needed to train these robots. We developed a system for extracting human motion data and displaying them graphically.We extracted motion data using a Perception Neuron motion capture system and used the Unity engine for the simulation. Several experiments were performed to demonstrate the accuracy of the extracted motion data.Of the various methods used to collect human motion data, markerless motion capture is highly inaccurate, while optical motion capture is very expensive, requiring several high-resolution cameras and a large number of markers. Motion capture using a magnetic field sensor is subject to environmental interference. Therefore, we used an inertial motion capture system. Each movement sequence involved four and was repeated 10 times. The data were stored and standardized. The motions of three individuals were compared to those of a reference person; the similarity exceeded 90% in all cases. Our rehabilitation robot accurately simulated human movements: individually tailored wearable robots could be designed based on our data. Safe and stable robot operation can be verified in advance via simulation. Walking stability can be increased using walking robots trained via machine learning algorithms.

scholarly journals Use of computer innovative technologies in improving the dartsmen training

2020 ◽  
Vol 26 ◽  
pp. 00061
Author(s):  
Elina Makarova ◽  
Vladislav Dubatovkin ◽  
Nataliya Berezinskaya ◽  
Lyudmila Barkhatova ◽  
Elena Oleynik
Keyword(s):  
Motion Capture ◽  
Body Parts ◽  
Visual Material ◽  
Video Cameras ◽  
Dart Throwing ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Effective Use ◽  
Near Future ◽  
Motion Capture Systems

The research is focused on studying the possibility of effective use of the dart grip system, the work of the athlete’s hand, to prepare the dartsman for competitions using the MOSAR complex. The experiment uses optical motion capture systems, a set of video cameras, led parameter sensors, and devices that allow to record the movement of body parts and a dart. This method of training and controlling dart throwing can serve as educational and visual material for training future athletes. The use of such motion capture systems in the near future may become one of the main aspects of training, both beginners and professionals, in many sports.

Human Motion Capture Driven by Orientation Measurements

1999 ◽  
Vol 8 (2) ◽  
pp. 187-203 ◽  
Author(s):  
Tom Molet ◽  
Ronan Boulic ◽  
Daniel Thalmann
Keyword(s):  
Motion Capture ◽  
Human Motion ◽  
High Rate ◽  
Position Measurement ◽  
Human Motion Capture ◽  
Fast Tracking ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Tracking Object ◽  
Capture Techniques

Motion-capture techniques are rarely based on orientation measurements for two main reasons: (1) optical motion-capture systems are designed for tracking object position rather than their orientation (which can be deduced from several trackers), (2) known animation techniques, like inverse kinematics or geometric algorithms, require position targets constantly, but orientation inputs only occasionally. We propose a complete human motion-capture technique based essentially on orientation measurements. The position measurement is used only for recovering the global position of the performer. This method allows fast tracking of human gestures for interactive applications as well as high rate recording. Several motion-capture optimizations, including the multijoint technique, improve the posture realism. This work is well suited for magnetic-based systems that rely more on orientation registration (in our environment) than position measurements that necessitate difficult system calibration.

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