Investigating the performance of an athlete and monitoring them is important to athletes and coaches because it can provide information about the training progress and injury recovery. Coaches are not always on side when athletes doing their training, so a device which is small and easy to use will increase the monitored training sessions significantly and allow the athlete to compare multiple training sessions. This technology has seen rapid development over the last years and is now available in very small sizes which have little effect on the swimmer. Using this kind of sensor, kinematics can be measured and extracted for stroke characteristics investigations. This research used an sacrum mounted self-developed inertial sensor, providing a triaxial accelerometer, gyroscope and RF capability in a waterproof casing to investigate the arm symmetry and swimming velocity in freestyle swimming. The recorded data were filtered using a high-pass Hamming windowed FIR filter to remove the gravity components from the wanted signal. The acceleration signal was then processed to find the velocity of the swimmer. A zero-crossing detection algorithm was used to investigate the arm symmetry and to find the individual stroke rates (SR), the differences between left and right arm stroke durations and the left and right arm stroke velocities (asymmetry). The investigation of the left and right arm velocity patterns of each individual swimmer allowed the investigation of each swimmers asymmetry in propulsion and velocity for the individual arms. The results of the mean lap velocities showed that the two different methods agree with an R2 of 0.92. Furthermore a conducted Bland-Altman analysis showed a low bias of -0.04 with an upper limit of agreement of 0.03 and a lower limit of agreement of -0.11.Griffith Sciences, Griffith School of EngineeringFull Tex

This research used an sacrum mounted self developed inertial sensor, which provides triaxial accelerometers and gyroscopes, RF capability and 2GB on-board memory in a waterproof casing to investigate the arm symmetry in freestyle swimming. The recorded acceleration data was filtered using a high-pass Hamming windowed FIR filter to remove the sensor orientation from the wanted signal. The signal was then analyzed using a zero-crossing detection algorithm to find the individual stroke rates (SR) and the differences between left and right arm stroke durations (asymmetry).

Athlete monitoring is a major field of interest for professional and recreational runners as well as for coaches to improve performance and reduce injury risk. The development of inertial sensors in recent years offers the opportunity to improve the number of monitored training sessions significantly. This research used a self-developed inertial sensor in conjunction with a motion tracking system and four smart bands to record the runner’s movement and extract parameters such as step numbers and frequencies. The data recorded were calibrated before it was high-pass filtered to remove gravity components from the signal. A peak detection algorithm was developed to find the number of steps, which have been further used to compare the different systems (IMU, motion capture, smart bands) and find their agreement. The results showed a very strong correlation between the IMU and the motion tracking system of r² = 0.998, and an r² = 0.996 between the IMU and one smart band.

Athlete monitoring and performance investigations are crucial for athletes and coaches as part of training progress and injury recovery. Athlete training is not always conducted at special training facilities even at some points without a coach being able to take part at every training session. Small IMUs are offering a great benefit as they allow recording nearly every conducted training session. The research presented here used a self-developed IMU in comparison with a tethered velocity meter to investigate the arm symmetry in freestyle swimming. The recorded data were firstly calibrated before it was high-pass filtered to remove gravity from the signal. A zero-crossing detection algorithm was applied to allow the separation into left- and right-arm strokes to find variations in distances. The results showed a very strong agreement between the IMU and the velocity meter of r² > 0.99 for each individual athlete with a mean agreement over all participants of r² = 0.9994.