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Regardless of the type of sport, coaches must perform the difficult task of evaluating the performance of athletes. In some cases, this task is aided using technology which provides tools for this purpose. When the sport considered is taekwondo this scenario does not apply as the athlete evaluation methods used are mostly manual. Thus, the project presented in this paper has the main objective to develop a system that can be used as a tool to evaluate the performance of taekwondo athletes in real time, with special attention to the low cost of implementation and ease of use. With the intention of meeting these requirements, the developed system comprises a 3D camera with a depth sensor (Orbbec Astra), Inertial Measurement Units (IMUs) with accelerometer and gyroscope, a computer and specific software developed for this purpose. This system allows the collection of data from the athletes’ movements necessary for the creation of a dataset that is then analyzed and interpretated. The system permits the user to obtain real-time information about the speed, acceleration, and strength of the athlete’s limbs during training as well as the identification of some movements and their accounting. To achieve this functionality, deep-learning architecture models were used, more specifically long short-term memory (LSTM). The intention is to provide a new training methodology through faster feedback, so that a faster evolution of the athlete’s performance is possible, contributing to the technological development of the assessment practices used in taekwondo.
Cunha et al. (Sun,) studied this question.