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

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.

Simple and efficient thermal calibration for MEMS gyroscopes.

Gyroscopes are now becoming one of the most sold MEMS sensors, given that the many applications that require their use are booming. In the medical field, gyroscopes can be found in Inertial Measurement Units used for the development of clinical tools that are dedicated to human-movement monitoring. However, MEMS gyroscopes are known to suffer from a drift phenomenon, which is mainly due to temperature variations. This drift dramatically affects measurement capability, especially that of cheap MEMs gyroscopes. Calibration is therefore a key factor in achieving accurate measurements. However, traditional calibration procedures are often complex and require costly equipment. This paper therefore proposes an easy protocol for performing a thermal gyroscope calibration. In this protocol, accuracy over the angular velocity is evaluated by referring to an optoelectronic measurement, and is compared with the traditional calibration performed by the manufacturer. The RMSE between the reference angular velocity and that obtained with the proposed calibration was of 0.7°/s, which was slightly smaller than the RMSE of 1.1°/s achieved by the manufacturer's calibration. An analysis of uncertainty propagation shows that offset variability is the major source of error over the computed rate of rotation from the tested sensors, since it accounts for 97% of the error. It can be concluded that the proposed simple calibration method leads to a similar degree of accuracy as that achieved by the manufacturer's procedure.

Comparison of calibration methods for accelerometers used in human motion analysis.

In the fields of medicine and biomechanics, MEMS accelerometers are increasingly used to perform activity recognition by directly measuring acceleration; to calculate speed and position by numerical integration of the signal; or to estimate the orientation of body parts in combination with gyroscopes. For some of these applications, a highly accurate estimation of the acceleration is required. Many authors suggest improving result accuracy by updating sensor calibration parameters. Yet navigating the vast array of published calibration methods can be confusing. In this context, this paper reviews and evaluates the main measurement models and calibration methods. It also gives useful recommendations for better selection of a calibration process with regard to a specific application, which boils down to a compromise between accuracy, required installation, algorithm complexity, and time.