A Hidden Markov Model of the breaststroke swimming temporal phases using wearable inertial measurement units

The recent advances in wearable inertial sensors opened a new horizon for pervasive measurement of human locomotion even in aquatic environment. In this paper we proposed an automatic approach of detecting the key temporal events of breaststroke swimming as a tentatively explored technique due to the complexity of the stroke. We used two inertial measurement units worn on the right arm and right leg of seven swimmers to capture the kinematics of the breaststroke. The detection of the temporal phases from the inertial signals was undertaken in the framework of a Hidden Markov Model (HMM). Supervised learning of the HMM parameters was achieved using the reference data from manual video analysis by an expert. The outputs of two well-known classifiers on the inertial signals were fused to unfold the input space of the HMM for an enhanced performance. An average correct phase detection of 93.5% for the arm stroke, 94.4% for the leg stroke and the minimum precision of 67 milliseconds in detection of the key events, suggests the accuracy of the method.