• International journal of advanced smart convergence
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• v.7 no.3
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• pp.66-72
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• 2018

In recent years, indoor simulated sports have become widely used, and screen baseball system has emerged that can play baseball in indoor space. In this paper, we propose a virtual pitching system that can improve the realism of screen baseball game. This virtual pitching system is characterized in that it uses a transmissive screen in the form of a pitching machine without a pitching hole and installed on the back of the screen. Therefore, unlike existing systems where pitching holes are formed on the screen, it enhances the immersion feeling of displayed images. Also, in this pitching system, the synchronization algorithm between the pitching machine and the virtual pitcher is used to form a sense of unity between the virtual pitcher and the ball according to various types of virtual pitchers, thereby enhancing the reality of baseball games.

• Journal of Sensor Science and Technology
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• v.27 no.6
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• pp.380-385
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• 2018

Recently, interest in baseball has been increasing as the level of international baseball games, the popularity of domestic leagues, and the number of players entering the MLB has increased. In this paper, we propose a pitching system that can be applied to both professional and amateur baseball. The pitching system consists of a control module using MSB764T PLC, a pitching mechanism including AC motors and a ball feed rail, an HMI using the CHA-070WR model, inverter, etc. To pitch the breaking balls, the two AC motors each use an inverter to independently control the speed. The implemented pitching system was experimented on, investigating ball speed and ball movement according to RPM using the BUSHNELL Velocity Speed Gun. Experimental results on ball speed are similar to the theoretical data and the measured data. From the experimental data, it is confirmed that the damping coefficient value for the pitching ball is about 0.98. In the case of the breaking ball, the larger the difference between the speeds on the sides of the ball and the faster the ball speed, the larger the bending degree.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
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• v.29 no.2
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• pp.39-48
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• 2023

Background: The position of pitcher requires a lot of repetitive motion, and because of this, it is known that not only professional baseball players, but also middle and high school players are frequently exposed to injuries in baseball. The purpose of this study is to examine the differences in upper extremity muscle activity during repeated pitching and the activity of each muscle during repeated pitching by analyzing middle and high school pitchers, divided into groups by age. Methods: Twenty participants (10 middle school male students and 10 high school male students) were recruited for this research. The outcome measures included neuromuscular motor control, including the upper trapezius (UT), triceps brachii (TB), deltoid (DT), latissimus dorsi (LD), biceps brachii (BB), pectoralis major(PM), extensor carpi radialis(ER), and flexor carpi radialis (FR). Results: The two-way analysis of varaince (ANOVA) was used to compare the muscle activity variables between the middle school and high school students. The one-way ANOVA was used to compare the muscle activity variables within time differences each groups. Conclusion: Our results provided promising clinical evidence that guide for upper extremity muscles to increase pitching efficiency in middle and high school base ball players.

• Clinics in Shoulder and Elbow
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• v.19 no.1
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• pp.15-19
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• 2016

Background: To investigate how many rookie pitchers suffered from injuries while playing in the amateur league without guidelines for prevention of excessive pitching, we analyzed their amateur pitching patterns based on the pitch count, use of breaking balls, and pitches during winter camp. Methods: Forty-one rookie pitchers who graduated from high school or university in 2013 and joined professional baseball teams. Participants were interviewed by a trainer using our questionnaire. Injury inclusion criteria were 1) history of shoulder surgery, 2) history of elbow surgery, 3) shoulder pain requiring treatment, and 4) elbow pain requiring treatment. Results: Mean number of pitches per game and warm-up pitches for practice was 84.5 pitches (range, 15 to 130 pitches) and 16.4 pitches (range, 2 to 210 pitches), respectively. Mean number of pitches during the last year was 906.9 (range, 80 to 2,000). Mean number of maximal pitches was 127 pitches (range, 50 to 210 pitches). Fourteen pitchers had pitched over 150 pitches. Twenty-seven pitchers (65.9%) had pitched in spite of enduring pain. During winter training (mean 1.8 months), mean number of pitches per day was 162.5 pitches, and 20 pitchers (48.8%) had practiced pitching excessively despite the cold weather. Twenty-six rookies (63.4%) had shoulder pain or history of shoulder surgery, and 31 pitchers (75.6%) had elbow pain or history of elbow surgery. Only four participants (9.8%) did not have pain and history of surgery. Conclusions: For young baseball pitchers, guidelines for prevention of excessive pitching and for regulating the winter training program may be needed.

• Korean Journal of Applied Biomechanics
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• v.30 no.1
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• pp.1-6
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• 2020

Objective: The incidence rate of elbow ulnar collateral ligament injuries is dependent on the throwing speed or pitching type, especially in adolescent baseball players. However, mixed results have been reported due to a lack of controlled biomechanical analysis. Thus, the purpose of this study was to investigate the biomechanical analysis of the elbow in relation to throwing speed and pitching type. Method: Four overhead type high-school baseball players were recruited for this study. The participants were asked to throw balls with different types of pitch and speed. While the throwing speeds were measured, each pitching moment of the elbow was recorded. Descriptive statistics, frequency analysis, mean comparison analysis, and Pearson's correlation analysis were performed in order to examine differences in peak varus and valgus moment during pitching motion in the elbow in all throwing speed and pitching types. Results: There was no significant difference in physical characteristics, throwing speed, and momentum variability among all players. The mean varus moments were 44.38±1.55 Nm, 48.83±1.66 Nm, and 48.94±0.95 Nm, and the moment gaps between varus and valgus were 7.36±3.25 Nm, 7.44±2.02 Nm, and 7.36±2.62 Nm in fastball, curveball, and slider ball, respectively. The varus moment was higher in the curved and slider balls than in the fastballs, and there was no significant differences between the varus moments regarding the pitching type. However, the increase in valgus moment and decrease in moment gap according to throwing speed was significantly increased in the slider ball (r=0.718 and -0.591, respectively). Conclusion: The possibility of elbow injury caused by the valgus moment or moment gapincreases more rapidly in slider balls as the speed increases. Based on our results, appropriate pitching guidelines should be suggested to prevent ulnarligament injuries, especially in adolescent baseball players.

• Korean Journal of Applied Biomechanics
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• v.17 no.4
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• pp.127-139
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• 2007

The purpose of this study was to examine the pitching ability of baseball player by analysing the effect of elastic resistance exercise and pilates exercise on EMG. Five out of currently active highschool baseball player performed 5 times a week of elastic resistance exercise and pilates for 8 weeks, each for 100 minutes. They demonstrated significant increase in EMG after 8 weeks of exercise. Especially, in extensor carpi radialis brevis and flexor carpi radialis of fore arm, they demonstrated significant increase of inning figure after exercise. These results suggested that elastic resistance exercise and pilates exercise can improve pitching ability of baseball player according to inning.

• Korean Journal of Applied Biomechanics
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• v.17 no.1
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• pp.81-90
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• 2007

The effort identifying positioning information of the moving object in real time has been a issue not only in sport biomechanics but also other academic areas. In order to solve this issue, this study tried to track the movement of a pitched ball that might provide an easier prediction because of a clear focus and simple movement of the object. Machine learning has been leading the research of extracting information from continuous images such as object tracking. Though the rule-based methods in artificial intelligence prevailed for decades, it has evolved into the methods of statistical approach that finds the maximum a posterior location in the image. The development of machine learning, accompanied by the development of recording technology and computational power of computer, made it possible to extract the trajectory of pitched baseball from recorded images. We present a method of baseball tracking, based on object tracking methods in machine learning. We introduce three state-of-the-art researches regarding the object tracking and show how we can combine these researches to yield a novel engine that finds trajectory from continuous pitching images. The first research is about mean shift method which finds the mode of a supposed continuous distribution from a set of data. The second research is about the research that explains how we can find the mode and object region effectively when we are given the previous image's location of object and the region. The third is about the research of representing data into features that we can deal with. From those features, we can establish a distribution to generate a set of data for mean shift. In this paper, we combine three works to track baseball's location in the continuous image frames. From the information of locations from two sets of images, we can reconstruct the real 3-D trajectory of pitched ball. We show how this works in real pitching images.

• Smart Media Journal
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• v.13 no.4
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• pp.16-22
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• 2024

Pitching is a major part of baseball, so much so that it can be said to be the beginning of baseball. Analysis of accurate pitching motions is very important in terms of performance improvement and injury prevention. When analyzing the correct pitching motion, the currently used motion capture method has several critical environmental drawbacks. In this paper, we propose analysis of pitching motion using the RGB-based Human Pose Estimation (HPE) model to replace motion capture, which has these shortcomings, and use motion capture data and HPE data to verify its reliability. The similarity of the two data was verified by comparing joint coordinates using the Dynamic Time Warping (DTW) algorithm.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.1
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• pp.130-136
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• 2017

The three-wheel pitching machine is a device that throws balls automatically instead of a pitcher and is used chiefly to train baseball players. The machine is abundantly used by people in indoor baseball grounds for baseball games. However, in Korea, foreign products are more popular because the efficiency of domestic products is poor as compared to that of the foreign ones. Therefore, a miniature pitching machine was manufactured to analyze and solve the problems of the existing machine. We added a feeder device to insert the balls in the machine and developed a smart phone application. The machine is easily controlled by a smart phone with bluetooth. While manufacturing the miniature, the existing problems were mitigated and the machine was redesigned for mass production. This study attempted to render the pitching machine more convenient and safer as a substitute for foreign pitching machines.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.485-492
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• 2014

In baseball, the traditional measure of pitchers are wins and ERA. But these statistics are influenced by luck or team power. So sabermetrician proposes a number of indicators that predict future performance. We determine a new measure, which we call pitching grade index (PGI) that efficiently summarizes a pitcher's performance on a numerical scale using principal components analysis. The PGI statistic can often be useful to assessing a pitcher's individual contribution. Also K-means clustering algorithm are used for segmentation of players into groups.