Recognition of Butterfly strokes using different Machine Learning Models

Swimming is a lifelong beneficial activity. It is an excellent training since it requires you to move your entire body against the water’s resistance; however, by the time, these movements may not be in a right way. In addition, the wrong movements may lead to many pains such as shoulder pain, elbow pain and lower back pain especially in difficult strokes. The coach is the one who instructs the swimmers and tell them which is incorrect, and which is correct. However, he can’t recognize all the incorrect movements, so this needs an instructor who can see all the stroke’s mistakes. Hence our proposed system, which uses machine learning techniques, utilizes four different models which are Long short-term memory (LSTM), k-nearest neighbor (Knn), for time series 1-${\$}$ recognizer and Dynamic time wrapping (DTW) to detect the incorrect butterfly stroke. The system uses an accelerometer and gyroscope sensors to detect and evaluate correct and Incorrect swimming patterns in butterfly stoke. In addition to attaching a mobile application to the swimmer’s wrist which gathers all data which allows the coach and the swimmer to know the incorrect strokes such as lifting the head too high, sweeping out after hand entry, and bending the arm. When an incorrect movement is recognized. DTW achieved the best accuracy among all classifiers which are 80.5%. The system helps in aiding the coaches to know all the swimmer’s performance and all his performance, and also aid the intermediate swimmers to know more about his performance to enhance it.