系統識別號 U0026-2708201509402500
論文名稱(中文) 應用光學式體感科技輔助肩關節運動功能性量化評估-以棒球選手為例-
論文名稱(英文) Using the Kinect Technology to Quantify the Shoulder Motion Function for Baseball Player
校院名稱 成功大學
系所名稱(中) 生物醫學工程學系
系所名稱(英) Department of BioMedical Engineering
學年度 104
學期 1
出版年 104
研究生(中文) 何政育
研究生(英文) Cheng-Yu Ho
學號 P86024158
學位類別 碩士
語文別 英文
論文頁數 84頁
口試委員 指導教授-葉明龍
中文關鍵字 棒球  生物力學  動作評估  傷害評估  體感科技 
英文關鍵字 Baseball  Biomechanics  Movement assessment  Injury assessment  Kinect Technology 
中文摘要 手肘關節以及肩關節是棒球選手常見的運動傷害。現階段國內校園培訓球隊仍然缺乏運動傷害專業防護員的介入來保護選手避免運動傷害的發生。棒球運動相關的體適能、運動學與動力學評估,主要使用高速攝影機、以及力板來偵測並加以評量,該模式涉及高度的專業性與主觀性,因此,本研究目的為應用光學體感科技進行動作偵測與量化評估,透過建立以微軟公司Kinect感應器為基礎之量測運算平台來偵測人體20個關節點及運動軌跡,藉以及時偵知棒球選手的運動表現。
英文摘要 Elbow and shoulder joints are the common sports injury sites for baseball players So far, it still lacks athletic trainer intervention to protect the athletes from injury. Traditionally, the assessment of the physical fitness, kinematic parameter and kinetic parameter were by the high speed motion capture and force plate, but this method required expansive and complicate equipment and professional persons to conduct the evaluation. It is not able to become popular assessment approaches and reach all the athletes required assistance. Hence, a simpler and cheaper motion assessment method is needed to reach all level of athletes. The Kinect sensor developed by the Microsoft to capture the 20 skeletal joint marker and trajectory from the human body is an affordable tool to conduct the motion tracking. This study used Kinect to monitor the youth baseball player’s exercise performance immediately.
The specific goals are as below: 1) Develop the shoulder motion record and analysis system, which can automatically capture the kinematic parameter to quantify and analyze the shoulder function for the baseball players; 2) Conduct the system calibration to correlate the joint angle between the real position and value recorded by Kinect for their accuracy and precision; 3) Develop the shoulder exercise norm for athletes and perform the case study to evaluate whether the player is at the risk of the exercise injury from exercise performance; and 4) Develop the assessment computing platform and provide the protection system to monitor the player’s exercise performance immediately.
The study is aim to compare the difference between the joint angle, angular velocity, angular acceleration and time difference before and after the exercise training. The results showed that: 1) The calibration regression show a polynomial equation can be used to correlate the joint angle at different plane of volunteer and the angles recorded from the Kinect, so the joint position and angle and joint movement can be detected accurately; 2) The exercise norm developed by this study of the can help the coach realize the exercise performance, health condition and rehabilitation progress of athletes immediately; 3) In the case study, the system can analyze the player’s muscle power, muscular endurance and balance before and after the exercise training, as well as analyze the player’s stability, explosive power, proprioception and the pitch type. This research develops the economic method to quantify the gross motion and assess the possibility of injury and performance.
論文目次 Table of Contents
中文摘要 I
Abstract II
Table of Contents VI
List of Tables VIII
List of Figures X
Chapter 1 Introduction 1
1.1. Motivation and Background 1
1.2. Function and Anatomy of Upper Extremity 1
1.2.1. Wrist Joint 3
1.2.2. Elbow Joint 4
1.2.3. Shoulder Joint 5
1.2.4. Overhead Throwing Motion 8
1.3. Mechanism of Joint Injury on Upper extremity 10
1.3.1. Common Injury of Upper Extremity 11
1.3.2. Shoulder Injury Mechanism 13
1.3.3. Overhead Throwing Exercise injury 14
1.4. Clinical Assessment and Treatment 15
1.4.1. Treatment Strategy and Method 20
1.5. Biomechanical Dynamic Analysis 20
1.5.1. High speed motion capture 21
1.5.2. Kinect Technology Application 22
1.6. Study Aims and Purpose 25
Chapter 2 Materials and Methods 26
2.1. Subjects 26
2.2. Instruments 26
2.2.1. Kinect Technology Measurement 26
2.2.2. Software 29
2.3. Procedure 30
2.3.1. Environmental Settlement 31
2.4. System Calibration 31
2.4.1. Joint Position Calibration for Z Axis 32
2.4.2. Joint Angle Calibration 34
2.5. Develop Exercise Norm for Baseball Player 35
2.6. Using Kinect for Case Study 40
2.7. Statistical Analysis 41
Chapter 3 Results 42
3.1. System Calibration Test 42
3.1.1. Joint Position Calibration for Z Axis 42
3.1.2. Joint Angle Calibration 46
3.2. Develop Exercise Norm for Baseball Player 50
3.3. Using Kinect for Case Study 58
Chapter 4 Discussion 61
4.1. System Calibration Test 61
4.1.1. Calibration for distance for Z Axis 61
4.1.2. Joint Angle Calibration 62
4.2. Develop Exercise Norm for Baseball Player 63
4.3. Using Kinect for Case Study 65
4.4. Limitation 69
Chapter 5 Conclusion and Future Work 70
5.1 Conclusion 70
5.2 Reference to Future Work 71
References 72
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