Magnetic Angular Rate Gravity Research Articles

Benchmarking Dataset of Signals from a Commercial MEMS Magnetic-Angular Rate-Gravity (MARG) Sensor Manipulated in Regions with and without Geomagnetic Distortion.

In this paper, we present the FIU MARG Dataset (FIUMARGDB) of signals from the tri-axial accelerometer, gyroscope, and magnetometer contained in a low-cost miniature magnetic-angular rate-gravity (MARG) sensor module (also known as magnetic inertial measurement unit, MIMU) for the evaluation of MARG orientation estimation algorithms. The dataset contains 30 files resulting from different volunteer subjects executing manipulations of the MARG in areas with and without magnetic distortion. Each file also contains reference ("ground truth") MARG orientations (as quaternions) determined by an optical motion capture system during the recording of the MARG signals. The creation of FIUMARGDB responds to the increasing need for the objective comparison of the performance of MARG orientation estimation algorithms, using the same inputs (accelerometer, gyroscope, and magnetometer signals) recorded under varied circumstances, as MARG modules hold great promise for human motion tracking applications. This dataset specifically addresses the need to study and manage the degradation of orientation estimates that occur when MARGs operate in regions with known magnetic field distortions. To our knowledge, no other dataset with these characteristics is currently available. FIUMARGDB can be accessed through the URL indicated in the conclusions section. It is our hope that the availability of this dataset will lead to the development of orientation estimation algorithms that are more resilient to magnetic distortions, for the benefit of fields as diverse as human-computer interaction, kinesiology, motor rehabilitation, etc.

HMM Based Adaptive Kalman Filter for Orientation Estimation

An adaptive orientation estimation method based on hidden Markov model (HMM) using multiple Magnetic Angular Rate Gravity (MARG) sensor is proposed to address the problem of linear acceleration and ferromagnetic disturbance. Two parallel Kalman filters containing first-order Gauss-Markov disturbance models are implemented to estimate the disturbances before estimating the orientation. It is important that the noise covariance of the disturbance model should match the magnitude of the disturbance to ensure the estimation performance; otherwise, the disturbance estimation accuracy will reduce significantly. Therefore, an adaptive Kalman filter based on HMM is designed to adjust the disturbance noise covariance. After the disturbances are separated, the estimated gravity acceleration and geomagnetic field as well as the gyroscope measurement are taken as the inputs, and the orientation estimation procedure is established by using the multiplicative extended Kalman filter (MEKF). Experiments show that the proposed method achieves a higher level of accuracy in comparison with other orientation estimation methods under various conditions.

Towards Robust Robot Control in Cartesian Space Using an Infrastructureless Head- and Eye-Gaze Interface.

This paper presents a lightweight, infrastructureless head-worn interface for robust and real-time robot control in Cartesian space using head- and eye-gaze. The interface comes at a total weight of just 162 g. It combines a state-of-the-art visual simultaneous localization and mapping algorithm (ORB-SLAM 2) for RGB-D cameras with a Magnetic Angular rate Gravity (MARG)-sensor filter. The data fusion process is designed to dynamically switch between magnetic, inertial and visual heading sources to enable robust orientation estimation under various disturbances, e.g., magnetic disturbances or degraded visual sensor data. The interface furthermore delivers accurate eye- and head-gaze vectors to enable precise robot end effector (EFF) positioning and employs a head motion mapping technique to effectively control the robots end effector orientation. An experimental proof of concept demonstrates that the proposed interface and its data fusion process generate reliable and robust pose estimation. The three-dimensional head- and eye-gaze position estimation pipeline delivers a mean Euclidean error of mm for head-gaze and mm for eye-gaze at a distance of 0.3–1.1 m to the user. This indicates that the proposed interface offers a precise control mechanism for hands-free and full six degree of freedom (DoF) robot teleoperation in Cartesian space by head- or eye-gaze and head motion.

Performance Analysis of a Head and Eye Motion-Based Control Interface for Assistive Robots.

Assistive robots support people with limited mobility in their everyday life activities and work. However, most of the assistive systems and technologies for supporting eating and drinking require a residual mobility in arms or hands. For people without residual mobility, different hands-free controls have been developed. For hands-free control, the combination of different modalities can lead to great advantages and improved control. The novelty of this work is a new concept to control a robot using a combination of head and eye motions. The control unit is a mobile, compact and low-cost multimodal sensor system. A Magnetic Angular Rate Gravity (MARG)-sensor is used to detect head motion and an eye tracker enables the system to capture the user’s gaze. To analyze the performance of the two modalities, an experimental evaluation with ten able-bodied subjects and one subject with tetraplegia was performed. To assess discrete control (event-based control), a button activation task was performed. To assess two-dimensional continuous cursor control, a Fitts’s Law task was performed. The usability study was related to a use-case scenario with a collaborative robot assisting a drinking action. The results of the able-bodied subjects show no significant difference between eye motions and head motions for the activation time of the buttons and the throughput, while, using the eye tracker in the Fitts’s Law task, the error rate was significantly higher. The subject with tetraplegia showed slightly better performance for button activation when using the eye tracker. In the use-case, all subjects were able to use the control unit successfully to support the drinking action. Due to the limited head motion of the subject with tetraplegia, button activation with the eye tracker was slightly faster than with the MARG-sensor. A further study with more subjects with tetraplegia is planned, in order to verify these results.

SteadEye-Head-Improving MARG-Sensor Based Head Orientation Measurements Through Eye Tracking Data.

This paper presents the use of eye tracking data in Magnetic AngularRate Gravity (MARG)-sensor based head orientation estimation. The approach presented here can be deployed in any motion measurement that includes MARG and eye tracking sensors (e.g., rehabilitation robotics or medical diagnostics). The challenge in these mostly indoor applications is the presence of magnetic field disturbances at the location of the MARG-sensor. In this work, eye tracking data (visual fixations) are used to enable zero orientation change updates in the MARG-sensor data fusion chain. The approach is based on a MARG-sensor data fusion filter, an online visual fixation detection algorithm as well as a dynamic angular rate threshold estimation for low latency and adaptive head motion noise parameterization. In this work we use an adaptation of Madgwicks gradient descent filter for MARG-sensor data fusion, but the approach could be used with any other data fusion process. The presented approach does not rely on additional stationary or local environmental references and is therefore self-contained. The proposed system is benchmarked against a Qualisys motion capture system, a gold standard in human motion analysis, showing improved heading accuracy for the MARG-sensor data fusion up to a factor of 0.5 while magnetic disturbance is present.

Formulation of a new gradient descent MARG orientation algorithm: Case study on robot teleoperation

Abstract We introduce a novel magnetic angular rate gravity (MARG) sensor fusion algorithm for inertial measurement. The new algorithm improves the popular gradient descent (ʻMadgwick’) algorithm increasing accuracy and robustness while preserving computational efficiency. Analytic and experimental results demonstrate faster convergence for multiple variations of the algorithm through changing magnetic inclination. Furthermore, decoupling of magnetic field variance from roll and pitch estimation is proven for enhanced robustness. The algorithm is validated in a human-machine interface (HMI) case study. The case study involves hardware implementation for wearable robot teleoperation in both Virtual Reality (VR) and in real-time on a 14 degree-of-freedom (DoF) humanoid robot. The experiment fuses inertial (movement) and mechanomyography (MMG) muscle sensing to control robot arm movement and grasp simultaneously, demonstrating algorithm efficacy and capacity to interface with other physiological sensors. To our knowledge, this is the first such formulation and the first fusion of inertial measurement and MMG in HMI. We believe the new algorithm holds the potential to impact a very wide range of inertial measurement applications where full orientation necessary. Physiological sensor synthesis and hardware interface further provides a foundation for robotic teleoperation systems with necessary robustness for use in the field.

Analysis and Visualization of 3D Motion Data for UPDRS Rating of Patients with Parkinson's Disease.

Remote monitoring of Parkinson’s Disease (PD) patients with inertia sensors is a relevant method for a better assessment of symptoms. We present a new approach for symptom quantification based on motion data: the automatic Unified Parkinson Disease Rating Scale (UPDRS) classification in combination with an animated 3D avatar giving the neurologist the impression of having the patient live in front of him. In this study we compared the UPDRS ratings of the pronation-supination task derived from: (a) an examination based on video recordings as a clinical reference; (b) an automatically classified UPDRS; and (c) a UPDRS rating from the assessment of the animated 3D avatar. Data were recorded using Magnetic, Angular Rate, Gravity (MARG) sensors with 15 subjects performing a pronation-supination movement of the hand. After preprocessing, the data were classified with a J48 classifier and animated as a 3D avatar. Video recording of the movements, as well as the 3D avatar, were examined by movement disorder specialists and rated by UPDRS. The mean agreement between the ratings based on video and (b) the automatically classified UPDRS is 0.48 and with (c) the 3D avatar it is 0.47. The 3D avatar is similarly suitable for assessing the UPDRS as video recordings for the examined task and will be further developed by the research team.

6DOF Wireless Tracking Wand Using MARG and Vision Sensor Fusion

We present a low cost battery-powered 6-degree-of-freedom wireless wand for 3D modeling in free space by tri-axis Magnetic, Angular Rate, Gravity (MARG) and vision sensor fusion. Our approach has two stages of sensor fusion, each with different algorithms for finding 3D orientation and position. The first stage fusion algorithm, a complementary filter, utilizes MARG sensors to compute 3D orientation relative to the direction of gravity and earth's magnetic field in a quaternion format, which was adjusted with compensations for magnetic distortion. The second stage fusion algorithm, a Kalman filter, utilizes accelerometer data and IR marker velocity to compute 3D position. In order to compute the IR marker linear velocity along the optical axis (the z-axis), we present a simple and efficient image-based technique to find the distance of the object from the camera using blob area pixels in the image. Our fusion (inside-in and outside-in) approach efficiently solves short time occlusion, needs of frequent calibration, and unbounded drift problems involved in numerical integration of inertial sensors data and improves the degrees of freedom at low cost without compromising accuracy. The results are compared with a leading commercial magnetic motion tracking system to demonstrate the performance of the wand.

Development of an innovative low-cost MARG sensors alignment and distortion compensation methodology for 3D scanning applications

Working in the low cost 3D scanner design domain, it would be very interesting to employ the inertial technologies because they could provide objects’ surface spatial data, recording their movements, and asking a very low cost in term of sensor investment. Unfortunately these technologies are characterized by distortion problems that normally do not allow to obtain satisfying measures for being employed for 3D scanning applications.This situation happens when working with Magnetic Angular Rate Gravity (MARG) sensor, on which many reports have been written to describe the methods used to suitably manage the data provided by the sensors in order to obtain an accurate orientation estimation; but only a few address the problem of calibration and distortion compensation. Furthermore, the proposed approaches usually involve both complex sensors models and accurate calibration facilities expensive from the workload, the computational and the economic points of view which compromise their possible employment in low-cost 3D scanning applications.In this paper, a novel approach for MARG sensors heading alignment and distortion compensation is proposed in order to increase the reliability of the information provided by the sensors and improve the process of attitude estimation, in order to get measurement quality level sufficient to be employable in 3D scanning applications.Both the effectivity and the reliability of the proposed approach are validated by some experimental results and the performances are evaluated considering the quality of the outcome provided by the same attitude estimation algorithm processing raw data and compensated data.

Attitude Measurement and Optimization for Horizontal Directional Drilling Based on MARG Sensor

Attitude measurement of the drill bit is the key for horizontal directional drilling, which determines the final positioning accuracy. As for disadvantages of existing algorithms, a method is proposed to get the azimuth, the inclination, and the tool face angle based on gyros. In order to improve attitude measurement accuracy, a quaternion-based attitude complementary filter algorithm is adopted. The accelerometer and magnetometer are used to measure the components of the earth’s gravity field and magnetic field to compensate for gyro drift errors over time, while the gyro ensures the stability of the drill with dynamic acceleration and enhances the robustness of the system. Detailed analysis and simulation results are presented. Results show that the quaternion-based attitude complementary filter algorithm using multiple MARG (Magnetic, Angular Rate, Gravity) sensors can effectively reduce the gyro drift errors and make the attitude measurement error less than +/-0.3 degree.