Optimistic prediction method for value counter of physical exercises repetitions on the output signal of the neural network

O. Danchenko, J. Broyda, O. Bielova

In recent years, due to the rapid development of artificial intelligence systems and computer vision new neural network architectures appeared, which main function is to assess the three-dimensional posture of a person on one video stream. Posture assessment, signal analysis, and result generation for the end-user inevitably take some time, while in some cases the end-user needs immediate feedback. The author proposes a method of optimistic prediction of exercise repetitions count before the end of a spe-cific iteration of the exercise during the analysis of the neural network output signal, which estimates the three-dimensional position of a person. An example of the method is given on the basis of the exercise "squats", the calculation of which is performed using a state machine. This method is based on the addition of the exercise iteration counting state machine. This addition increments the counter in the middle of the exercise and performs the decrement if the exercise is declared invalid after the end of the exercise. Application of this method in algo-rithms of the analysis of exercise will allow emitting number of exercise repetitions without delay as the human coach does. This method is not specific to any particular neural network and therefore can be used at the output of almost any system that analyzes the sequence of positions of human joints in space using a state machine. The article also presents the test results of the proposed method. The proposed test method can be applied to any cyclic exercise.

Keywords: optimistic prediction; neural networks; computer vision; biomechanics; Artificial Intelligence; squat; state machine; instant repetition counter.