Sensor Combination Selection Strategy for Kayak Cycle Phase Segmentation Based on Body Sensor Networks

Motion capture technology has been widely used in the sport analysis to improve their performance and reduce the injury risk. Kayak, a popular outdoor sport, employs the coordination of multiple muscles and skeletons, especially those of upper limbs that must be investigated carefully. The fine-time phase segmentation of rowing cycle plays an important role in analyzing kayaker's technique. Aiming at the problem of laborious manual phase labeling in the traditional video analysis method, an automatic phase segmentation method for kayak rowing is proposed combined with a machine learning algorithm. In this article, inertial sensors and a data fusion algorithm are used to calculate the joint angles between arm and trunk, left elbow and right elbow when the athlete is rowing. According to the permutation and combination principle, the angle sequence is combined in nine different ways, and four machine learning algorithms (decision tree, support vector machine, k-nearest neighbor, bagging ensemble learning) are used to study the effects of different combinations on rowing phase division. Among them, the precision of phase segmentation becomes higher with the increase of motion information. The combination of arm to trunk joint angle only needs three data collection nodes; thus, the computational cost is smaller; moreover, all the four algorithms show good classification accuracy (up to 98.1%). The results indicating that the combination of arm to trunk joint angle and support vector machine algorithm could better complete the task of the phase segmentation for kayak rowing.

Automated summary

This paper proposes an automatic phase segmentation method combined with a machine learning algorithm for analyzing kayak rowing. The results indicate that the combination of arm to trunk joint angle and support vector machine algorithm can better complete the task of phase segmentation.