Trampoline Motion Decomposition Method Based on Deep Learning Image Recognition

The automatic segmentation and classification of an unknown motion data stream based on given motion classes constitute an important research problem with applications in computer vision, animation, healthcare, and sports sciences. In this paper, the scenario of trampoline motions is considered, where an athlete performs a routine consisting of sequence of jumps that belong to predefined motion classes such as somersaults. The purpose of this study was to make theoretical discussions on the turning starting time and starting technique of trampoline somersault based on image recognition and point out that the appropriate turning starting time of trampoline somersault is the event when the spring net of the trampoline recovers and applies force to the human body, and the overturning start exists in the latter half of the take-off action. It is considered that how to obtain the ideal full reaction force of the net facing the human body is the flip starting technique. This work analyzes the key steps and events for trampoline somersaults and the application of artificial intelligence for the recognition of actions in the healthcare and sports fields. The effectiveness of the proposed study is shown through experimental results. The study can facilitate the process of recognition of trampoline somersault.