Abstract:
In sports, coaching remains an essential aspect of the efficiency of the athlete’s performance. This paper proposes a wrist wearable assistant for the swimmer called iSwimCoach. The key aim behind the system is to detect and analyze incorrect swimming patterns in a free crawl swimming style using an accelerometer sensor. iSwimCoach collects patterns of a swimmer’s stream which enables it to detect the strokes to be analyzed in real-time. Therefore, introducing quick and efficient self-coaching feature for mid-level athlete to enhance their swimming style. In our research, we were able to monitor athlete strokes underwater and hence assist swimming coaches. The proposed system was able to classify four types of strokes done by mid-level players (correct strokes, wrong recovery, wrong hand entry and wrong high elbow). The system informs both the swimmer and the coach when an incorrect movement is detected. iSwimCoach achieved 91% accuracy for the detection and classification of incorrect strokes by a fast non expensive dynamic time warping algorithm. These readings analyzed in real-time to automatically generate reports for the swimmer and coach.
