Real-Time Boxing Feedback Using Human Pose Estimation and Machine Learning : A Mobile Application for AI Body-Tracking, Computer Vision, and Machine Learning

Sammanfattning

In the evolving field of sports technology, the involvement of Artificial Intelligence to enhance the training of professional athletes is becoming increasingly popular. A common mistake in boxing is to lower the hand after a throwing jab, especially if the jab is followed by a cross. Despite the recent advancements in Artificial Intelligence and sports technology, its application in boxing training is largely an unexplored territory. The significance of this issue combined with the potential for Artificial Intelligence in sports training highlights the relevancy of this project. This thesis presents the development and evaluation of a mobile phone application that utilizes Artificial Intelligence to provide real-time feedback on a boxers guard position. The coordinates of specific key-points of the body is continuously being checked by an algorithm and if the wrists are below a certain threshold, audio feedback is given. In order to only give feedback after a jab has been thrown, a machine learning model was trained to detect a punch. Data was gathered through testing of the system and a participant survey which were then analysed to reach a conclusion. The results from the test show that the system is 95.33 percent accurate at detecting when a guard is mistakenly lowered, while being 72.67 percent accurate at detecting that a lowered guard was intentional. The system is 96.33 percent accurate in not giving feedback when the guard is up. The survey results show that the system is effectively doing what it is supposed to do and could be used as an alternative or complement to having a coach present. This opens up the possibilities for similar systems to be developed for correcting all kinds of mistakes in boxing or other sports.