Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output

Abstract This paper presents an extended Kalman filter for pose estimation using noise covariance matrices based on sensor output. Compact and lightweight nine-axis motion sensors are used for motion analysis in widely various fields such as medical welfare and sports. A nine-axis motion sensor includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. Information obtained from the three sensors is useful for estimating joint angles using the Kalman filter. The extended Kalman filter is used widely for state estimation because it can estimate the status with a small computational load. However, determining the process and observation noise covariance matrices in the extended Kalman filter is complicated. The noise covariance matrices in the extended Kalman filter were found for this study based on the sensor output. Postural change appears in the gyroscope output because the rotational motion of the joints produces human movement. Therefore, the process noise covariance matrix was determined based on the gyroscope output. An observation noise covariance matrix was determined based on the accelerometer and magnetometer output because the two sensors’ outputs were used as observation values. During a laboratory experiment, the lower limb joint angles of three participants were measured using an optical 3D motion analysis system and nine-axis motion sensors while participants were walking. The lower limb joint angles estimated using the extended Kalman filter with noise covariance matrices based on sensor output were generally consistent with results obtained from the optical 3D motion analysis system. Furthermore, the lower limb joint angles were measured using nine-axis motion sensors while participants were running in place for about 100 seconds. The experiment results demonstrated the effectiveness of the proposed method for human pose estimation.

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Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output

ROBOMECH Journal ◽

10.1186/s40648-020-00185-y ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Ayuko Saito ◽

Satoru Kizawa ◽

Yoshikazu Kobayashi ◽

Kazuto Miyawaki

Keyword(s):

Kalman Filter ◽

Motion Analysis ◽

Extended Kalman Filter ◽

Pose Estimation ◽

Lower Limb ◽

Covariance Matrices ◽

Motion Sensors ◽

Sensor Output ◽

Joint Angles ◽

Noise Covariance

Abstract This paper presents an extended Kalman filter for pose estimation using noise covariance matrices based on sensor output. Compact and lightweight nine-axis motion sensors are used for motion analysis in widely various fields such as medical welfare and sports. A nine-axis motion sensor includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. Information obtained from the three sensors is useful for estimating joint angles using the Kalman filter. The extended Kalman filter is used widely for state estimation because it can estimate the status with a small computational load. However, determining the process and observation noise covariance matrices in the extended Kalman filter is complicated. The noise covariance matrices in the extended Kalman filter were found for this study based on the sensor output. Postural change appears in the gyroscope output because the rotational motion of the joints produces human movement. Therefore, the process noise covariance matrix was determined based on the gyroscope output. An observation noise covariance matrix was determined based on the accelerometer and magnetometer output because the two sensors’ outputs were used as observation values. During a laboratory experiment, the lower limb joint angles of three participants were measured using an optical 3D motion analysis system and nine-axis motion sensors while participants were walking. The lower limb joint angles estimated using the extended Kalman filter with noise covariance matrices based on sensor output were generally consistent with results obtained from the optical 3D motion analysis system. Furthermore, the lower limb joint angles were measured using nine-axis motion sensors while participants were running in place for about 100 s. The experiment results demonstrated the effectiveness of the proposed method for human pose estimation.

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Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output

10.21203/rs.3.rs-29523/v1 ◽

2020 ◽

Author(s):

AYUKO SAITO ◽

Satoru Kizawa ◽

Yoshikazu Kobayashi ◽

Kazuto Miyawaki

Keyword(s):

Kalman Filter ◽

Motion Analysis ◽

Extended Kalman Filter ◽

Pose Estimation ◽

Lower Limb ◽

Covariance Matrices ◽

Motion Sensors ◽

Sensor Output ◽

Joint Angles ◽

Noise Covariance

Abstract This paper presents an extended Kalman filter for pose estimation using noise covariance matrices based on sensor output. Compact and lightweight nine-axis motion sensors are used for motion analysis in widely various fields such as medical welfare and sports. A nine-axis motion sensor includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. Information obtained from the three sensors is useful for estimating joint angles using the Kalman filter. The extended Kalman filter is used widely for state estimation because it can estimate the status with a small computational load. However, determining the process and observation noise covariance matrices in the extended Kalman filter is complicated. The noise covariance matrices in the extended Kalman filter were found for this study based on the sensor output. Postural change appears in the gyroscope output because the rotational motion of the joints produces human movement. Therefore, the process noise covariance matrix was determined based on the gyroscope output. An observation noise covariance matrix was determined based on the accelerometer and magnetometer output because the two sensors’ outputs were used as observation values. During a laboratory experiment, the lower limb joint angles of three participants were measured using an optical 3D motion analysis system and nine-axis motion sensors while participants were walking. The lower limb joint angles estimated using the extended Kalman filter with noise covariance matrices based on sensor output were generally consistent with results obtained from the optical 3D motion analysis system. Furthermore, the lower limb joint angles were measured using nine-axis motion sensors while participants ran on a treadmill for about 90 seconds. The experiment results demonstrated the effectiveness of the proposed method for human pose estimation.

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

Sensors ◽

10.3390/s18103490 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3490 ◽

Cited By ~ 3

Author(s):

Alexis Nez ◽

Laetitia Fradet ◽

Frédéric Marin ◽

Tony Monnet ◽

Patrick Lacouture

Keyword(s):

Kalman Filter ◽

Motion Analysis ◽

Human Motion ◽

Covariance Matrices ◽

Human Motion Analysis ◽

Orientation Estimation ◽

Optoelectronic System ◽

Whole Process ◽

Noise Covariance ◽

Measurement Units

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.

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Physically Consistent Whole-Body Kinematics Assessment Based on an RGB-D Sensor. Application to Simple Rehabilitation Exercises

Sensors ◽

10.3390/s20102848 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2848

Author(s):

Jessica Colombel ◽

Vincent Bonnet ◽

David Daney ◽

Raphael Dumas ◽

Antoine Seilles ◽

...

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Joint Angle ◽

Statistical Parametric Mapping ◽

Biomechanical Model ◽

Practical Method ◽

Covariance Matrices ◽

Whole Body ◽

Joint Angles ◽

Optimal Tuning

This work proposes to improve the accuracy of joint angle estimates obtained from an RGB-D sensor. It is based on a constrained extended Kalman Filter that tracks inputted measured joint centers. Since the proposed approach uses a biomechanical model, it allows physically consistent constrained joint angles and constant segment lengths to be obtained. A practical method that is not sensor-specific for the optimal tuning of the extended Kalman filter covariance matrices is provided. It uses reference data obtained from a stereophotogrammetric system but it has to be tuned only once since it is task-specific only. The improvement of the optimal tuning over classical methods in setting the covariance matrices is shown with a statistical parametric mapping analysis. The proposed approach was tested with six healthy subjects who performed four rehabilitation tasks. The accuracy of joint angle estimates was assessed with a reference stereophotogrammetric system. Even if some joint angles, such as the internal/external rotations, were not well estimated, the proposed optimized algorithm reached a satisfactory average root mean square difference of 9.7 ∘ and a correlation coefficient of 0.8 for all joints. Our results show that an affordable RGB-D sensor can be used for simple in-home rehabilitation when using a constrained biomechanical model.

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Simulation Analysis of Extended Kalman Filter Applied for Estimating Position and Speed of a Brushless DC Motor

Power Electronics and Drives ◽

10.2478/pead-2018-0008 ◽

2018 ◽

Vol 3 (1) ◽

pp. 145-155

Author(s):

Maciej Chojowski

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Simulation Analysis ◽

Covariance Matrices ◽

Brushless Dc Motor ◽

Bldc Motor ◽

Rotor Speed ◽

State Equations ◽

Brushless Dc ◽

Noise Covariance

Abstract The purpose of this paper was to present a method for the estimation of the rotor speed and position of brushless DC (BLDC) motor. The BLDC motor state equations were developed, and the model was discretised. Extended Kalman filter has been designed to observe specific states from the state vector, needed for the sensorless control (rotor position) and to determine the speed, which may be useful to use as a feedback for the controller. A test was carried out to determine the noise covariance matrices in a simulation manner.

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Knee joint angle estimation by sequential correction of gyroscope bias

10.21203/rs.3.rs-697958/v1 ◽

2021 ◽

Author(s):

AYUKO SAITO ◽

YUTAKA TANZAWA ◽

SATORU KIZAWA

Keyword(s):

Knee Joint ◽

Medical Care ◽

Motion Analysis ◽

Pose Estimation ◽

Joint Angle ◽

Angle Measurement ◽

Motion Sensors ◽

Joint Angles ◽

Knee Joint Angle ◽

Tangential Acceleration

Abstract Compact and lightweight nine-axis motion sensors have come to be used for motion analysis in a variety of fields such as medical care, welfare, and sports. Nine-axis motion sensors include a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer and can estimate joint angles using the gyroscope outputs. However, the bias of the gyroscope is often unstable depending on the measurement environment and the accuracy of the gyroscope itself, causing error to accumulate in the angle obtained by integrating the gyroscope output. Although several sensor fusions have been proposed for pose estimation, such as using an accelerometer and a magnetometer, sequentially estimating and correcting the bias of the gyroscope are desirable for more accurate pose estimation. In addition, considering accelerations other than the acceleration due to gravity is important for a sensor fusion method that utilizes the accelerometer to correct the gyroscope output. Therefore, in this study, an extended Kalman filter algorithm was developed to sequentially correct both the gyroscope bias and the centrifugal and tangential acceleration of an accelerometer. The gait measurement results indicate that the proposed method successfully suppresses drift in the estimated knee joint angle over the entire measurement time of knee angle measurement during gait. The knee joint angles estimated using the proposed method were generally consistent with results obtained from an optical 3D motion analysis system. The proposed method is expected to be useful for estimating motion in medical care and welfare applications.

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Structure selection of noise covariance matrices for linear Kalman filter design

2020 European Control Conference (ECC) ◽

10.23919/ecc51009.2020.9143850 ◽

2020 ◽

Author(s):

Federico Bianchi ◽

Simone Formentin ◽

Luigi Piroddi

Keyword(s):

Kalman Filter ◽

Filter Design ◽

Covariance Matrices ◽

Structure Selection ◽

Noise Covariance ◽

Selection Of

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Attitude Estimation Using Kalman Filtering: External Acceleration Compensation Considerations

Journal of Sensors ◽

10.1155/2016/6943040 ◽

2016 ◽

Vol 2016 ◽

pp. 1-24 ◽

Cited By ~ 5

Author(s):

Romy Budhi Widodo ◽

Chikamune Wada

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Attitude Estimation ◽

Estimation Accuracy ◽

Compensation Model ◽

Measurement Noise Covariance ◽

Noise Covariance ◽

The One ◽

The Right ◽

External Acceleration

Attitude estimation is often inaccurate during highly dynamic motion due to the external acceleration. This paper proposes extended Kalman filter-based attitude estimation using a new algorithm to overcome the external acceleration. This algorithm is based on an external acceleration compensation model to be used as a modifying parameter in adjusting the measurement noise covariance matrix of the extended Kalman filter. The experiment was conducted to verify the estimation accuracy, that is, one-axis and multiple axes sensor movement. Five approaches were used to test the estimation of the attitude: (1) the KF-based model without compensating for external acceleration, (2) the proposed KF-based model which employs the external acceleration compensation model, (3) the two-step KF using weighted-based switching approach, (4) the KF-based model which uses thethreshold-basedapproach, and (5) the KF-based model which uses the threshold-based approach combined with a softened part approach. The proposed algorithm showed high effectiveness during the one-axis test. When the testing conditions employed multiple axes, the estimation accuracy increased using the proposed approach and exhibited external acceleration rejection at the right timing. The proposed algorithm has fewer parameters that need to be set at the expense of the sharpness of signal edge transition.

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