scholarly journals Measurements of the Flight Trajectory of a Spinning Soccer Ball and the Magnus Force Acting on It

Proceedings ◽  
2020 ◽  
Vol 49 (1) ◽  
pp. 88
Author(s):  
Takeshi Asai ◽  
Kaoru Kimachi ◽  
Richong Liu ◽  
Masaaki Koido ◽  
Masao Nakayama ◽  
...  

The trajectory of a soccer ball, kicked with a spin to curve it into the goal, is strongly influenced by aerodynamic factors such as the Magnus force. Several studies using a wind-tunnel and high-speed cameras have investigated the Magnus force acting on a spinning soccer ball. However, the exact effect of the Magnus force on the trajectory of a spinning soccer ball in free flight remains unclear. This study set out to use an optical three-dimensional motion-capture system to record the details of the flight of such a spinning soccer ball. The maximum curvature of the ball’s trajectory occurred in the middle of its flight. The sideways-directed Magnus force acting on the ball decreased as the ball’s speed decreased during the entire flight. Thus, it was concluded that the deflection of the trajectory of the ball decreases as the sideways-acting force decreases throughout the flight.

Author(s):  
Jay Ryan U. Roldan ◽  
Dejan Milutinović ◽  
Zhi Li ◽  
Jacob Rosen

In this paper, we propose a quantitative approach based on identifying hand trajectory dissimilarities through the use of a multidimensional scaling (MDS) analysis. A high-rate motion capture system is used to gather three-dimensional (3D) trajectory data of healthy and stroke-impacted hemiparetic subjects. The mutual dissimilarity between any two trajectories is measured by the area between them. This area is used as a dissimilarity variable to create an MDS map. The map reveals a structure for measuring the difference and variability of individual trajectories and their groups. The results suggest that the recovery of hemiparetic subjects can be quantified by comparing the difference and variability of their individual MDS map points to the points from the cluster of healthy subject trajectories. Within the MDS map, we can identify fully recovered patients, those who are only functionally recovered, and those who are either in an early phase of, or are nonresponsive to the therapy.


Author(s):  
Pyeong-Gook Jung ◽  
Sehoon Oh ◽  
Gukchan Lim ◽  
Kyoungchul Kong

Motion capture systems play an important role in health-care and sport-training systems. In particular, there exists a great demand on a mobile motion capture system that enables people to monitor their health condition and to practice sport postures anywhere at any time. The motion capture systems with infrared or vision cameras, however, require a special setting, which hinders their application to a mobile system. In this paper, a mobile three-dimensional motion capture system is developed based on inertial sensors and smart shoes. Sensor signals are measured and processed by a mobile computer; thus, the proposed system enables the analysis and diagnosis of postures during outdoor sports, as well as indoor activities. The measured signals are transformed into quaternion to avoid the Gimbal lock effect. In order to improve the precision of the proposed motion capture system in an open and outdoor space, a frequency-adaptive sensor fusion method and a kinematic model are utilized to construct the whole body motion in real-time. The reference point is continuously updated by smart shoes that measure the ground reaction forces.


2020 ◽  
Vol 14 ◽  
Author(s):  
Grady W. Jensen ◽  
Patrick van der Smagt ◽  
Egon Heiss ◽  
Hans Straka ◽  
Tobias Kohl

2011 ◽  
Vol 19 ◽  
pp. 214-219 ◽  
Author(s):  
Yi-Hong Lin ◽  
Wen-Hong Wu ◽  
Wei-Zhe Huang

Author(s):  
Shohei Shibata ◽  
Kiyoshi Hirose ◽  
Takeshi Naruo ◽  
Yuichi Shimizu

This study aimed to (a) develop an algorithm that could estimate a baseball bat trajectory from the beginning of the swing to the follow-through phase during a practice swing without a ball and (b) evaluate the accuracy of the proposed method using a three-dimensional motion capture system. The sensor fusion using the adaptive Kalman filter for compensating velocity decreased the error of acceleration integration during the follow-through phase. Further, the three-dimensional bat trajectory in a global coordinate was estimated by combining the sensor fusion and compensation by motion characteristics. The three-dimensional bat trajectory from the swing beginning to the follow-through phase estimated by the proposed method was compared with the three-dimensional bat trajectory obtained by the three-dimensional motion capture system. The proposed method achieved a root mean square of the error of 7.72 km/h for velocity, which was less than the root mean square of the error (8.91 km/h) obtained by simple time integration of forward direction. These results indicate that the error by acceleration integration during the follow-through phase is compensated. The proposed method is, thus, deemed effective and can be used to evaluate baseball swing, including the follow-through phase, with high accuracy.


Author(s):  
Heather L. Lai ◽  
Susan Ko

Abstract This project focuses on the development and characterization of a high speed video motion capture system for the measurement of planar, rigid body motions. The ability to collect information related to the accelerations, velocities and positions of points on a rigid body as it moves in planar space is very important in the fields of science and engineering. Traditional techniques, including the use of accelerometers, extensors and lasers, either rely on contact between the rigid body and the sensor or only measure out of plane motion. In this project, an inexpensive monochromatic high speed camera was used in conjunction with markers adhered to the objects under investigation to measure the planar displacement of a point on a moving object. The high speed camera is able to capture video at a rate of up to 20,000 frames per second, however, at this speed the field of view is very small. For a larger field of view, the frames per second is diminished to close to 3,000 frames per second. The goal of this project was to develop the hardware parameters and software necessary to collect and process 2D motion data at different frequencies and then evaluate the efficacy of video motion capture through comparison with simultaneously captured acceleration data. The efficacy was evaluated over a range of accelerations using variable frequency oscillations. The video footage was processed, frame by frame in order to extract x and y position for the center of the marker. Extraction of the position data was completed using the MATLAB computer vision toolbox, which provides tools for identifying the x and y locations of corners, circle centers and other defining features. The project began by identifying size, shape, color and material of markers for effective data collection using the motion capture system. Additionally, camera settings, field of view, capture rate, lighting and mounting conditions were evaluated to determine what conditions would result in the most accurate position sensing. In order to validate the measurements from the motion capture system, position data were correlated with accelerations measured from a traditional accelerometer located on the object under test. In order for the position data collected through the high speed video capture to be compared with the acceleration data collected using measurement from accelerometers, numerical differentiation of the position signals gathered from the high speed footage was performed. The efficacy of different shape and size markers, along with other camera settings, will be demonstrated for specific oscillatory test profiles.


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