AI skipping is the core mode of this product. Through the changing curve of human skeleton points during exercise and the human skipping action, the algorithm can capture the various action details of people in the skipping process in complex scenarios. Combined with the action counting algorithm, it can analyze the skipping action cycle and perform intelligent timing and counting.
AI long jump uses computer vision algorithms to calculate the take-off area, take-off line and scale lines on the long jump mat, detects the take-off area and the human body to match the long jumper, and finally accurately calculates the long jump results through the human body motion trajectory curve and foot points. The AI technology used in the Winter Olympics ski jumping platform is used to digitize the long jump and analyze its movements, and the same set of software and hardware can be used to digitize multiple sports.
AI vertical jump high jump calculates the high jump result by obtaining the coordinates of the highest point of a person’s hand in space during the jump. The vertical jump and touch height test is simple and interesting. It can effectively train students’ lower limb explosiveness and improve their athletic performance. It can be easily completed during spare time, increasing students’ exercise time at school.
By calculating the key points of the human body’s hands, elbows, shoulders, knees, feet and other bones, it can intelligently count and judge the standard jumping jack movements and effectively record the number of jumping jacks. Jumping jacks can comprehensively improve physical fitness, strengthen cardiopulmonary function, enhance the heart’s blood supply capacity and the lungs’ oxygen supply capacity, activate the shoulder and arm muscles, and improve joint flexibility.
Based on convolutional neural networks, real-time detection of key points on the human body and intelligent video analysis are used to achieve intelligent timing counting and standardized exercise evaluation of three people doing sit-ups simultaneously under one camera.
Combining neural network analysis and deep learning algorithms for estimating human skeletons, the system intelligently captures human movements based on video to achieve intelligent timing and counting, intelligent error correction prompts, and annotated evaluation of three people doing pull-ups simultaneously under one camera. Help students evaluate their physical fitness through test scenarios and daily training scenarios. It is more intelligent, objective, and systematically recorded to evaluate students’ pull-up exercise tests.
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