scholarly journals Performance of Optical Structural Vibration Monitoring Systems in Experimental Modal Analysis

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1239
Author(s):  
Maksat Kalybek ◽  
Mateusz Bocian ◽  
Nikolaos Nikitas
Keyword(s):  
Modal Analysis ◽  
Motion Capture ◽  
Experimental Modal Analysis ◽  
Vibration Monitoring ◽  
Structural Vibration ◽  
Monitoring Systems ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Motion Capture Systems ◽  
Optical Vibration

Image-based optical vibration measurement is an attractive alternative to the conventional measurement of structural dynamics predominantly relying on accelerometry. Although various optical vibration monitoring systems are now readily available, their performance is currently not well defined, especially in the context of experimental modal analysis. To this end, this study provides some of the first evidence of the capability of optical vibration monitoring systems in modal identification using input–output measurements. A comparative study is conducted on a scaled model of a 3D building frame set in a laboratory environment. The dynamic response of the model to an impulse excitation from an instrumented hammer, and an initial displacement, is measured by means of five optical motion capture systems. These include commercial and open-source systems based on laser Doppler velocimetry, fiducial markers and marker-less pattern recognition. The performance of these systems is analysed against the data obtained with a set of high-precision accelerometers. It is shown that the modal parameters identified from each system are not always equivalent, and that each system has limitations inherent to its design. Informed by these findings, a guidance for the deployment of the considered optical motion capture systems is given, aiding in their choice and implementation for structural vibration monitoring.

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.

DEVELOPMENT OF MOTION CAPTURE SYSTEM USING DUAL VIDEO CAMERAS FOR THE GAIT DESIGN OF A BIPED ROBOT

2011 ◽  
Vol 08 (02) ◽  
pp. 275-299 ◽  
Author(s):  
JUNG-YUP KIM ◽  
YOUNG-SEOG KIM
Keyword(s):  
Motion Capture ◽  
Human Walking ◽  
Human Gait ◽  
Motion Capture System ◽  
System Calibration ◽  
Walking Gait ◽  
Video Cameras ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Motion Capture Systems

This paper, describes the development of a motion capture system with novel features for biped robots. In general, motion capture is effectively utilized in the field of computer animation. In the field of humanoid robotics, the number of studies attempting to design human-like gaits by using expensive optical motion capture systems is increasing. The optical motion capture systems used in these studies have involved a large number of cameras because such systems use small-sized ball markers; hence the position accuracy of the markers and the system calibration are very significant. However, since the human walking gait is a simple periodic motion rather than a complex motion, we have developed a specialized motion capture system for this study using dual video cameras and large band-type markers without high-level system calibration in order to capture the human walking gait. In addition to its lower complexity, the proposed capture method requires only a low-cost system and has high space efficiency. An image processing algorithm is also proposed for deriving the human gait data. Finally, we verify the reliability and accuracy of our system by comparing a zero moment point (ZMP) trajectory calculated by the motion captured data with a ZMP trajectory measured by foot force sensors.

scholarly journals Use of the Azure Kinect to measure foot clearance during obstacle crossing

2021 ◽  
Author(s):  
Kohei Yoshimoto ◽  
Masahiro Shinya
Keyword(s):  
Motion Capture ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Random Errors ◽  
Obstacle Crossing ◽  
Risk Of Falls ◽  
Optical Motion ◽  
Foot Clearance ◽  
Optical Motion Capture ◽  
Motion Capture Systems

Obstacle crossing is a typical adaptive locomotion known to be related to the risk of falls. Previous conventional studies have used elaborate and costly optical motion capture systems, which not only represent a considerable expense but also require participants to visit a laboratory. To overcome these shortcomings, we aimed to develop a practical and inexpensive solution for measuring obstacle-crossing behavior by using the Microsoft Azure Kinect, one of the most promising markerless motion capture systems. We validated the Azure Kinect as a tool to measure foot clearance and compared its performance to that of an optical motion capture system (Qualisys). We also determined the effect of the Kinect sensor placement on measurement performance. Sixteen healthy young men crossed obstacles of different heights (50, 150, and 250 mm). Kinect sensors were placed in front of and beside the obstacle as well as diagonally between those positions. As indices of measurement quality, we counted the number of measurement failures and calculated the systematic and random errors between the foot clearance measured by the Kinect and Qualisys. We also calculated the Pearson correlation coefficients between the Kinect and Qualisys measurements. The number of measurement failures and the systematic and random error were minimized when the Kinect was placed diagonally in front of the obstacle on the same side as the trail limb. The high correlation coefficient (r > 0.890) observed between the Kinect and Qualisys measurements suggests that the Azure Kinect has excellent potential for measuring foot clearance during obstacle-crossing tasks.

scholarly journals Performance of Camera-Based Vibration Monitoring Systems in Input-Output Modal Identification Using Shaker Excitation

2021 ◽  
Vol 13 (17) ◽  
pp. 3471
Author(s):  
Maksat Kalybek ◽  
Mateusz Bocian ◽  
Wojciech Pakos ◽  
Jacek Grosel ◽  
Nikolaos Nikitas
Keyword(s):  
Image Processing ◽  
Modal Analysis ◽  
Experimental Modal Analysis ◽  
Modal Identification ◽  
Vibration Monitoring ◽  
Mode Shapes ◽  
Monitoring Systems ◽  
Input Output ◽  
Engineering Structures ◽  
Scaled Model

Despite significant advances in the development of high-resolution digital cameras in the last couple of decades, their potential remains largely unexplored in the context of input-output modal identification. However, these remote sensors could greatly improve the efficacy of experimental dynamic characterisation of civil engineering structures. To this end, this study provides early evidence of the applicability of camera-based vibration monitoring systems in classical experimental modal analysis using an electromechanical shaker. A pseudo-random and sine chirp excitation is applied to a scaled model of a cable-stayed bridge at varying levels of intensity. The performance of vibration monitoring systems, consisting of a consumer-grade digital camera and two image processing algorithms, is analysed relative to that of a system based on accelerometry. A full set of modal parameters is considered in this process, including modal frequency, damping, mass and mode shapes. It is shown that the camera-based vibration monitoring systems can provide high accuracy results, although their effective application requires consideration of a number of issues related to the sensitivity, nature of the excitation force, and signal and image processing. Based on these findings, suggestions for best practice are provided to aid in the implementation of camera-based vibration monitoring systems in experimental modal analysis.

Non-rigid alignment pipeline applied to human gait signals acquired with optical motion capture systems and inertial sensors

2020 ◽  
Vol 98 ◽  
pp. 109429 ◽  
Author(s):  
Rubén Soussé ◽  
Jorge Verdú ◽  
Ricardo Jauregui ◽  
Ventura Ferrer-Roca ◽  
Simone Balocco
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Human Gait ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Motion Capture Systems

scholarly journals Concurrent assessment of gait kinematics using marker-based and markerless motion capture

2020 ◽  
Author(s):  
Robert Kanko ◽  
Elise Laende ◽  
Elysia Davis ◽  
W. Scott Selbie ◽  
Kevin J. Deluzio
Keyword(s):  
Motion Capture ◽  
Lower Limb ◽  
Video Data ◽  
Motion Capture System ◽  
Kinematic Data ◽  
Markerless Motion Capture ◽  
Common Marker ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Motion Capture Systems

AbstractKinematic analysis is a useful and widespread tool used in research and clinical biomechanics for the estimation of human pose and the quantification of human movement. Common marker-based optical motion capture systems are expensive, time intensive, and require highly trained operators to obtain kinematic data. Markerless motion capture systems offer an alternative method for the measurement of kinematic data with several practical benefits. This work compared the kinematics of human gait measured using a deep learning algorithm-based markerless motion capture system to those of a common marker-based motion capture system. Thirty healthy adult participants walked on a treadmill while data were simultaneously recorded using eight video cameras (markerless) and seven infrared optical motion capture cameras (marker-based). Video data were processed using markerless motion capture software, marker-based data were processed using marker-based capture software, and both sets of data were compared. The average root mean square distance (RMSD) between corresponding joints was less than 3 cm for all joints except the hip, which was 4.1 cm. Lower limb segment angles indicated pose estimates from both systems were very similar, with RMSD of less than 6° for all segment angles except those that represent rotations about the long axis of the segment. Lower limb joint angles captured similar patterns for flexion/extension at all joints, ab/adduction at the knee and hip, and toe-in/toe-out at the ankle. These findings demonstrate markerless motion capture can measure similar 3D kinematics to those from marker-based systems.

Sign in / Sign up

Export Citation Format

Share Document