Identification of Noise Covariance Matrices to Improve Orientation Estimation by Kalman Filter

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 10; p. 3490
• Main Authors: Nez, Alexis, Fradet, Laetitia, Marin, Frédéric, Monnet, Tony, Lacouture, Patrick
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 16.10.2018; MDPI AG
• Subjects: Biomechanics; covariance matrices; Engineering Sciences; human motion analysis; inertial sensors; Instrumentation and Detectors; Kalman filter; Mechanics; orientation measurement; Physics; inertial sensors; covariance matrices; orientation measurement; human motion analysis; Kalman filter; Orientation measurement; Inertial sensors; Human motion analysis; Covariance matrices
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18103490

Magneto-inertial measurement units (MIMUs) are a promising way to perform human motion analysis outside the laboratory. To do so, in the literature, orientation provided by an MIMU is used to deduce body segment orientation. This is generally achieved by means of a Kalman filter that fuses acceleration, angular velocity, and magnetic field measures. A critical point when implementing a Kalman filter is the initialization of the covariance matrices that characterize mismodelling and input error from noisy sensors. The present study proposes a methodology to identify the initial values of these covariance matrices that optimize orientation estimation in the context of human motion analysis. The approach used was to apply motion to the sensor manually, and to compare the orientation obtained via the Kalman filter to a measurement from an optoelectronic system acting as a reference. Testing different sets of values for each parameter of the covariance matrices, and comparing each MIMU measurement with the reference measurement, enabled identification of the most effective values. Moreover, with these optimized initial covariance matrices, the orientation estimation was greatly improved. The method, as presented here, provides a unique solution to the problem of identifying the optimal covariance matrices values for Kalman filtering. However, the methodology should be improved in order to reduce the duration of the whole process.