Objectives: There has been a big challenge for athletes at any level to reach their ultimate performance. According to a Motor Control theory, accurate programming of movements during the last stage of preparation to work out can improve athletes' performance. So, this study aims to design a performance analysis method that can be used to classify athletes and give them feedback during exercise. In this way, athletes and coaches can consider exercise schemes to modify and ameliorate athletes' performance.
Methods: The participants were made up of 20 men between 25 to 30 years old. These subjects include ten professional football players and ten amateur football players. The same trials in 6 successive days were done by all athletes, both professional and amateur. Two different types of conditions were designed for all participants. In the first three days, subjects were asked to shoot a penalty kick (football player) 10 times in an actual situation like what they do in a competition. These procedures were done in the simulation condition, in the next three days designed by the computer (Xbox). Surface electromyography (sEMG) was recorded from the gastrocnemius and tibialis anterior muscles during trials.
Results: Results showed the maximum error between basis vectors in all of the professional subjects in each situation, real and game, were 0.48 and 0.37, respectively (Mean square error index), which are very low relative to the range of data (5.1). In contrast, the minimum error among amateurs in each situation was 13.18 and 18.72, which are a high amount compared with the range of data (7).
Discussion: Professional athletes always, in both situations, use their muscles in the same way. In fact, professional athletes' muscles follow specific patterns due to slight errors, while amateurs' muscles do not.
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