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We challenged to get data about hand movement in pen spinning using MediaPipeHands and OpenCV. The purpose is to create a system that can be used toobjectively evaluate the performance of pen spinning competitions. Evaluationof execution, smoothness, and control in competitions are quite difficult andoften with subjectivity. Therefore, we aimed to fully automate the process byusing objective numerical values for evaluation. Uncertainty still exists inMediaPipe's skeletal recognition, and it tends to be more difficult torecognize in brightly colored backgrounds. However, we could improve therecognition accuracy by changing the saturation and brightness in the program.Furthermore, automatic detection and adjustment of brightness is now possible.As the next step to systematize the evaluation of pen spinning using objectivenumerical values, we adopted "hand movements". We were able to visualize theups and downs of the hand movements by calculating the standard deviation andL2 norm of the hand's coordinates in each frame. The results of hand movementsare quite accurate, and we feel that it is a big step toward our goal. In thefuture, we would like to make great efforts to fully automate the grading ofpen spinning.

Pen Spinning Hand Movement Analysis Using MediaPipe Hands