  This dissertation presents a methodology for recording speed climbing
training sessions with multiple cameras and annotating the videos with relevant
data, including body position, hand and foot placement, and timing. The
annotated data is then analyzed using deep learning techniques to create a
standard dataset of speed climbing training videos. The results demonstrate the
potential of the new dataset for improving speed climbing training and
research, including identifying areas for improvement, creating personalized
training plans, and analyzing the effects of different training methods.The
findings will also be applied to the training process of the Jiangxi climbing
team through further empirical research to test the findings and further
explore the feasibility of this study.
