The Bachelor thesis presents a simple method for determining the knee joint angle during cycling using inertial sensors, and its comparison to surface skeletal muscle activation with muscle contraction sensors.
To accompany the presented method, two sensor fusion methods are used: a simple complementary filter and an error-state Kalman filter.
Evaluation is achieved using an optical motion tracking system. For all three methods for a short measurement of up to 5 minutes the root mean square error is below 4,5°. Results from the Kalman filter proved to be the most stable, with a standard deviation under 1°. As such there was no measurement drift present in the Kalman filter measurements after the Kalman weights have converged and the root mean square error for all the conducted measurements stayed below 4,5°, in contrast to the basic and complementary filter measurements where a certain drift was always present.
Measurements of surface skeletal muscle activation with respect to the calculated knee joint angle indicate similar responses in comparison to an electromyogram, with certain artefacts present, caused by skin contraction and the effect of the Earth’s gravitational force on the muscles themselves.
The presented results indicate that the method is efficient in a laboratory environment and could be used for monitoring a cyclist’s position and in turn improving a cyclist’s technique and position and preventing certain injuries. Adopting the method presented, as opposed to optical motion capture systems, cheaper and more efficient solutions could be developed.
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