| dc.description.abstract | The main purpose of this research is to develop a machine learning mathematical model for the analysis of upper limb movements. This action analysis method is used to analyze the upper body posture of the user when performing various actions. It aims to eliminate redundant actions and correct wrong postures to achieve the effect of reducing fatigue, avoiding injury, and physical rehabilitation. It can be used in medical care and physical recovery. In addition to discussing clinical issues, fine motor analysis is also a major area of current sports science research. Measuring, recording, analyzing, and deconstructing the characteristics of specific sports movements, provides information such as movement linkage, muscle use sequence, rotation angle, and state of exertion for coaches and doctors to evaluate the athletes′ condition. Athletes can be provided with the most appropriate operating indicators of various muscles and bones to help them avoid sports injuries and enhance their performance within the physiological endurance. At present, motion analysis research in sports science is mainly done using optical motion capture systems and the recently popular wearable inertial measurement unit (IMU). However, the equipment used in the above techniques is relatively expensive, and the optical motion capture system also needs to be operated in a specialized laboratory environment. Therefore, the main research framework of this study is based on the need to reduce the equipment cost of scientific motion analysis and for ameliorating the limitation of the user′s operating position and range of motion. The main purpose of this research method is to perform real-time upper body motion analysis using only three accelerometers and two cameras. The acceleration sensing system is used to detect the movement and rotation trajectory of the human shoulder and elbow, and at the same time, two cameras with orthogonal fields of view will capture the movement of the specific positioning point of the human body. We developed a nonlinear regression model to model the image motion captured by the camera and the acceleration sensing data separately. Then, the acceleration model is mapped into the image model space to obtain the isomorphic relationship between the acceleration model and the image model. Finally, the pixel distance in the image space is corrected by the physical length of the specific positioning point, so the physical length relationship between the acceleration data points can be obtained. Therefore, the motion model of the upper body provided by the machine learning in this study can allow doctors to judge the patient′s muscle and bone condition, and can also be used to provide therapists with a basis for correct exercise posture. | en_US |