Efficient accelerometer-based swimming exercise tracking

Abstract

The study concentrates on tracking swimming exercises based on the data of 3D accelerometer and shows that human activities can be tracked accurately using low sampling rates. The tracking of swimming exercise is done in three phases: first the swimming style and turns are recognized, secondly the number of strokes are counted and thirdly the intensity of swimming is estimated. Tracking is done using efficient methods because the methods presented in the study are designed for light applications which do not allow heavy computing. To keep tracking as light as possible it is studied what is the lowest sampling frequency that can be used and still obtain accurate results. Moreover, two different sensor placements (wrist and upper back) are compared. The results of the study show that tracking can be done with high accuracy using simple methods that are fast to calculate and with a really low sampling frequency. It is shown that an upper back-worn sensor is more accurate than a wrist-worn one when the swimming style is recognized, but when the number of strokes is counted and intensity estimated, the sensors give approximately equally accurate results.