Abstract:
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. Dynamic
time wrappig(DTW) achieved the best accuracy among all classifiers which are an average
of 80.5%. The system’s main goal is to use an accelerometer and gyroscope from a mobile
sensors to detect and evaluate correct and incorrect swimming patterns in butterfly stoke.
We were able to track swimmers’ strokes underwater and therefore aid swimming coaches.
When an improper movement is recognised, the system alerts both the swimmer and the
coach. The key aim behind the system is to detect and analyze incorrect butterfly swimming
style patterns with mobile application attached to their wrist which collects patterns
of a swimmer’s stream which enables it to detect the incorrect strokes, count the correct
and incorrect strokes, count the total strokes which the swimmer made in specific distance.
