Use of ant colony optimization and the Kalman filter to deduce thigh dip angle via acceleration and angular velocity sensing

Abstract

Background: Acceleration and angular velocity sensors are commonly used in the measurement of gait parameters. A representative application calculates the limb segment dip angle. The rotation angle is typically deduced by a conventional Kalman filter, which includes the use of two empirically derived parameters. We improved this conventional method by introducing colony algorithm to find the optimal parameter combination instead of empirically assignment. Method: To achieve optimal results, a servo motor with an inertial measurement unit was used to simulate human limb segment motion according to programmed rotation angles that was employed as the ground truth. To minimize the bias between the ground truth and the calculated result, the ant colony algorithm was employed to obtain the optimal Kalman filter parameter combination in two-dimensional space. Results: By the motor experiment, the sum of the angle squared error was only 1.9305 rad2, much better than the 6.7723 rad2 error by the conventional method. The optimal parameter combination obtained was then used in a human experiment involving a basketball player. The frames from video of a whole gait cycle period were all showed with a corresponding deduced thigh dip angle curve diagram. Conclusion: The colony algorithm for parameters optimization results in less angle errors deduced by Kalman filter using the data from inertial measurement unit. The subject experiment verified the feasibility and performance of this method.