  We challenged to get data about hand movement in pen spinning using MediaPipe
Hands and OpenCV. The purpose is to create a system that can be used to
objectively evaluate the performance of pen spinning competitions. Evaluation
of execution, smoothness, and control in competitions are quite difficult and
often with subjectivity. Therefore, we aimed to fully automate the process by
using objective numerical values for evaluation. Uncertainty still exists in
MediaPipe's skeletal recognition, and it tends to be more difficult to
recognize in brightly colored backgrounds. However, we could improve the
recognition 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 objective
numerical values, we adopted "hand movements". We were able to visualize the
ups and downs of the hand movements by calculating the standard deviation and
L2 norm of the hand's coordinates in each frame. The results of hand movements
are quite accurate, and we feel that it is a big step toward our goal. In the
future, we would like to make great efforts to fully automate the grading of
pen spinning.
