Magnetic, Angular Rate, And Gravity Research Articles

Potential Use of Wearable Sensors to Assess Cumulative Kidney Trauma in Endurance Off-Road Running.

(1) Background: This study aimed to explore wearable sensors′ potential use to assess cumulative mechanical kidney trauma during endurance off-road running. (2) Methods: 18 participants (38.78 ± 10.38 years, 73.24 ± 12.6 kg, 172.17 ± 9.48 cm) ran 36 k off-road race wearing a Magnetic, Angular Rate and Gravity (MARG) sensor attached to their lower back. Impacts in g forces were recorded throughout the race using the MARG sensor. Two blood samples were collected immediately pre- and post-race: serum creatinine (sCr) and albumin (sALB). (3) Results: Sixteen impact variables were grouped using principal component analysis in four different principal components (PC) that explained 90% of the total variance. The 4th PC predicted 24% of the percentage of change (∆%) of sCr and the 3rd PC predicted the ∆% of sALB by 23%. There were pre- and post-race large changes in sCr and sALB (p ≤ 0.01) and 33% of participants met acute kidney injury diagnosis criteria. (4) Conclusions: The data related to impacts could better explain the cumulative mechanical kidney trauma during mountain running, opening a new range of possibilities using technology to better understand how the number and magnitude of the g-forces involved in off-road running could potentially affect kidney function.

Accident Detection System for Bicycle Riders

Bicycle riders are exposed to accident injuries such as head trauma. The risk of these riders' injuries is higher compared to the risk of injuries for motorists. Crashes, riders' errors, and environmental hazards are the cause of bicycle-related accidents. In 2017, nearly 50% of bicycle-related accidents occurred in urban areas at night, which may contribute to a delay in reporting the accidents to emergency centers. Hence, a system that can detect the accident is needed to notify urgent care clinics promptly. In this article, we propose a bicycle accident detection system. We designed hardware modules measuring the features related to the riding status of a bicycle and fall accidents. For this purpose, we used a magnetic, angular rate, and gravity (MARG) sensor-based system which measures four different types of signals: 1) acceleration, 2) angular velocity, 3) angle, and 4) magnitude of the riding status. Each of these signals is measured in three different directions (${X}$ , ${Y}$ , and ${Z}$ ). We used two different time-domain parameters, i.e., average and standard deviation. As a result, we considered 24 features. We used principal component analysis (PCA) for feature reduction and the support vector machines (SVM) algorithm for the detection of fall accidents. Experimental results show that our proposed system detects fall accidents during cycling status with 95.2% accuracy, which demonstrates the feasibility of our proposed bicycle accident detection system.

MARG Attitude Estimation Using Gradient-Descent Linear Kalman Filter

The magnetic, angular rate, and gravity (MARG) sensor array has been widely used for attitude estimation tasks. In this article, we report our new advances on related fusion algorithm based on the gradient-descent algorithm (GDA). Combining with complementary filters, GDA has been very popular for attitude estimation in industrial applications. The integration of the Kalman filter introduces covariance information and significantly improves in-run quality control. Some useful results are derived to build up the framework of a novel linear Kalman filter called GDA-LKF. A new simplified linear measurement quaternion model is proposed. This article also deals with the analytical adaptive problem of determining gradient-descent step length. We, for the first time, give the solution in the sense of least square based on aided vector measurements. Simulations are carried out to validate the noise sensitivity and covariance characteristics. The proposed schemes are also evaluated by real-world experiments that provide the audience with comparisons on accuracy, convergence, and execution time consumption performances between the proposed GDA-LKFs and recent representative methods. The results show that the proposed GDA-LKF can accurately estimate attitude with fast convergence and high accuracy. Note to Practitioners —Attitude estimation using magnetic, angular rate, and gravity (MARG) sensors is very common for ground, air, and underwater vehicles. The novel findings in this article aim to give the engineers a brand new perspective on the related filter design according to the widely employed gradient-descent algorithm (GDA) method. Some specific techniques, e.g., optimal adaptive law of the designed scheme, will also bring flexibility to the implementation with multiple considerations of object motions.

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots.

This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy.

A Novel Joint Angle Estimation Method for Serial Manipulator Using Micro-Electromechanical Systems Sensors

This article focuses on the use of low-cost micro-electromechanical systems (MEMS) magnetic, angular rate, and gravity (MARG) sensors in the field of estimating the joint angles of revolute serial manipulators. In particular, an easy-to-install joint angle estimation method, named the incremental decomposition method (IDM), is proposed. It allows the MEMS MARG sensors to be mounted in any attitude on any position of the manipulator's links, with at least one attitude measurement unit on the output side of each joint. The IDM is completely independent from the encoder-based joint angle estimator. Hence, it can work as an assistant fault-detection criterion for the economical robotic arms. A robotic arm with six degrees of freedom is used as the experimental setup. The IDM can estimate the manipulator's joint angles by fusing the attitude quaternion output by the MARG sensors. To validate the performances and limitations of this method, five experiments have been accomplished in this article. The experimental results are reported in order, in comparison with the high-resolution encoders.

Elasticity Measurement of Soft Tissues Using Hybrid Tactile and MARG-Based Displacement Sensor Systems

This paper presents a novel soft tissue elasticity measurement technique based on the fusion of Magnetic, Angular Rate, and Gravity (MARG) sensors and fixed tactile sensors. This paper is intended both as a stand-alone technology and as an extension of traditional tactile imaging of the breast to allow for accurate diagnosis of breast lesions. A series of artificial silicone materials known to imitate soft biological breast tissues is characterized using the proposed system and compared against an Instron universal testing machine to determine the system accuracy and repeatability. Comparing the characteristics of ten distinct materials, with elasticities in the range 9-90 kPa, determined by the proposed system to those from the Instron yields accuracy within 4% over the full-scale range. Interexperimental repeatability is within 1.5%. The proposed system delivers absolute elasticity of materials to within 4%, which, when combined with its lack of moving parts and low implementation cost, can significantly improve the diagnostic capability of tactile imaging in the clinical environment. By applying this technique, to determine the background elasticity of breast tissue, in conjunction with the relative lesion elasticity result from tactile arrays, the full non-invasive diagnostic potential of tactile imaging can be realized with the effect of reducing benign biopsy rates, secondary care costs, and patient stress.

A decision-tree based multiple-model UKF for attitude estimation using low-cost MEMS MARG sensor arrays

Micro-electronic-mechanical system (MEMS) is widely used in various applications, especially as a low-cost and small size system for attitude estimation which requires high accuracy and fast response. This work proposes a novel decision-tree based multiple-model unscented Kalman filter (DTMM-UKF) for attitude estimation. It is a quaternion-based attitude estimator that fuses related strap-down magnetic, angular rate, and gravity (MARG) sensor arrays. A set of novel criteria for testing whether the magnetometer and accelerometer are reliable is developed. To improve the anti-interference performance, we define four different filter models for the UKF. Particularly, a decision tree is established to automatically switch filter model based on these reliability test criteria. The priori attitude estimation is obtained from the process model using gyroscope data. Fusing the accelerometer and magnetometer data together, the observation attitude could be solved based on corresponding objective function and Jacobian matrix determined by the filter model. Under the UKF frame, the final optimal attitude could be determined by fusing priori estimation and observed attitude. Experimental tests show that the DTMM-UKF algorithm has better robustness and higher real-time estimation accuracy.

An instrumented glove for monitoring hand function.

The measurement of hand kinematics is important for the assessment and rehabilitation of the paralysed hand. The traditional method of hand function assessment uses a mechanical or electronic goniometer placed across the joint of interest to measure the range of joint movement. Mechanical goniometers are imprecise and lack the ability to provide a dynamic measurement; electronic goniometers are expensive and cumbersome to use during therapy. An alternative to the goniometric based assessment is to use inertial motion sensors to monitor the hand movement-these can be incorporated in a glove. In this paper, we present the design of an instrumented glove equipped with Magnetic, Angular Rate and Gravity (MARG) sensors for the objective evaluation of hand function. The instrumented glove presented in this paper is designed to assess the range of movement of the hand and also monitor the hand function during the course of hand rehabilitation. Static and dynamic calibrations were performed for the Euler angles calculated from the MARG sensors. The results are also presented for physiological flexion/extension of the wrist (relative roll), flexion/extension of elbow (relative pitch), and internal rotation/external rotation (relative yaw). The static calibration results gave mean absolute errors of 4.1° for roll, 4.0° for pitch, and 4.6° for yaw. From the dynamic calibration, the speed of response to a step change gave a convergence time of 0.4 s; sinusoidally oscillating movement gave good tracking at 0.2 Hz but exhibits overshoot errors at higher frequencies which were tested to be 1 Hz. We present the results of the calibration of the instrumented glove (one sensor pair measuring one joint angle) measuring anatomical joint angles-mean absolute errors during static calibration: 6.3° for a relative roll (wrist flexion/extension), 5.0° for relative pitch (elbow flexion/extension), and 4.5° for relative yaw (shoulder internal rotation/external rotation). The experimental results from the instrumented glove are promising, and it can be used as an alternative to the traditional goniometer based hand function assessments.

An Improved Yaw Estimation Algorithm for Land Vehicles Using MARG Sensors.

This paper presents a linear Kalman filter for yaw estimation of land vehicles using magnetic angular rate and gravity (MARG) sensors. A gyroscope measurement update depending on the vehicle status and constraining yaw estimation is introduced. To determine the vehicle status, the correlations between outputs from different sensors are analyzed based on the vehicle kinematic model and Coriolis theorem, and a vehicle status marker is constructed. In addition, a two-step measurement update method is designed. The method treats the magnetometer measurement update separately after the other updates and eliminates its impact on attitude estimation. The performances of the proposed algorithm are tested in experiments and the results show that: the introduced measurement update is an effective supplement to the magnetometer measurement update in magnetically disturbed environments; the two-step measurement update method makes attitude estimation immune to errors induced by magnetometer measurement update, and the proposed algorithm provides more reliable yaw estimation for land vehicles than the conventional algorithm.

Criterion Validity of a MARG Sensor to Assess Countermovement Jump Performance in Elite Basketballers.

Staunton, CA, Stanger, JJ, Wundersitz, DW, Gordon, BA, Custovic, E, and Kingsley, MI. Criterion validity of a MARG sensor to assess countermovement jump performance in elite basketballers. J Strength Cond Res 35(3): 797-803, 2021-This study assessed the criterion validity of a magnetic, angular rate, and gravity (MARG) sensor to measure countermovement jump (CMJ) performance metrics, including CMJ kinetics before take-off, in elite basketballers. Fifty-four basketballers performed 2 CMJs on a force platform with data simultaneously recorded by a MARG sensor located centrally on the player's back. Vertical accelerations recorded from the MARG sensor were expressed relative to the direction of gravity. Jumps were analyzed by a blinded assessor and the best jump according to the force platform was used for comparison. Pearson correlation coefficients (r) and mean bias with 95% ratio limits of agreement (95% RLOA) were calculated between the MARG sensor and the force platform for jumps performed with correct technique (n = 44). The mean bias for all CMJ metrics was less than 3%. Ninety-five percent RLOA between MARG- and force platform-derived flight time and jump height were 1 ± 7% and 1 ± 15%, respectively. For CMJ performance metrics before takeoff, impulse displayed less random error (95% RLOA: 1 ± 13%) when compared with mean concentric power and time to maximum force displayed (95% RLOA: 0 ± 29% and 1 ± 34%, respectively). Correlations between MARG and force platform were significant for all CMJ metrics and ranged from large for jump height (r = 0.65) to nearly perfect for mean concentric power (r = 0.95). Strong relationships, low mean bias, and low random error between MARG and force platform suggest that MARG sensors can provide a practical and inexpensive tool to measure impulse and flight time-derived CMJ performance metrics.

Adaptive EKF Based on HMM Recognizer for Attitude Estimation Using MEMS MARG Sensors

This paper addresses the intractable problem of attaining precise attitude estimation efficiently using MEMS MARG (magnetic, angular rate, and gravity) sensors for 3-D motion tracking, which is called attitude and heading reference system (AHRS). The performance of AHRS is adversely affected by sensor noise and measurement disturbances. To overcome this problem, we propose a novel adaptive extended Kalman filter (AEKF) in this paper, including a multiplicative extended Kalman filter (MEKF) and a hidden Markov Model (HMM) recognizer. MEKF is built on sensor noise model. An HMM recognizer is developed to identify the measurement disturbance caused by motion or environmental interference and then to adjust the noise covariance of MEKF adaptively. The proposed AEKF was assessed with a high-precision test platform by using a high-caliber commercial AHRS. Experimental results indicate that the proposed method achieves a higher level of stability and accuracy in comparison to other attitude estimators, especially in dynamic tests, which demonstrates the availability of 3-D motion tracking.

Heterogeneous data fusion for three-dimensional gait analysis using wearable MARG sensors

Gait analysis has become a research highlight. In this paper, we propose a computing method using wearable magnetic angular rate and gravity (MARG) sensor arrays with wireless network, which calculates absolute and relative orientation and position information of human foot motion during level walking and stair climbing process. Three-dimensional foot orientation and position were estimated by a Kalman-based sensor fusion algorithm and validated by ground truth provided by Vicon system. The repeatability of the alignment procedure and the measurement errors were evaluated on healthy subjects. Experimental results demonstrate that the proposed method has a good performance at both motion patterns. No significant drifts exist in the overall results presented in the paper. The measured and estimated information can be transmitted to remote server through internet. Moreover, this method could be applied to other cyclical activity monitoring.

Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter

Orientation estimation from magnetic, angular rate, and gravity (MARG) sensor array is a key problem in mechatronic-related applications. This paper proposes a new method in which a quaternion-based Kalman filter scheme is designed. The quaternion kinematic equation is employed as the process model. With our previous contributions, we establish the measurement model of attitude quaternion from accelerometer and magnetometer, which is later proved to be the fastest (computationally) one among representative attitude determination algorithms of such sensor combination. Variance analysis is later given enabling the optimal updating of the proposed filter. The algorithm is implemented on real-world hardware where experiments are carried out to reveal the advantages of the proposed method with respect to conventional ones. The proposed approach is also validated on an unmanned aerial vehicle during a real flight. Results show that the proposed one is faster than any other Kalman-based ones and even faster than some complementary ones while the attitude estimation accuracy is maintained.

Wi-Fi/MARG Integration for Indoor Pedestrian Localization.

With the wide deployment of Wi-Fi networks, Wi-Fi based indoor localization systems that are deployed without any special hardware have caught significant attention and have become a currently practical technology. At the same time, the Magnetic, Angular Rate, and Gravity (MARG) sensors installed in commercial mobile devices can achieve highly-accurate localization in short time. Based on this, we design a novel indoor localization system by using built-in MARG sensors and a Wi-Fi module. The innovative contributions of this paper include the enhanced Pedestrian Dead Reckoning (PDR) and Wi-Fi localization approaches, and an Extended Kalman Particle Filter (EKPF) based fusion algorithm. A new Wi-Fi/MARG indoor localization system, including an Android based mobile client, a Web page for remote control, and a location server, is developed for real-time indoor pedestrian localization. The extensive experimental results show that the proposed system is featured with better localization performance, with the average error 0.85 m, than the one achieved by using the Wi-Fi module or MARG sensors solely.

Fast Complementary Filter for Attitude Estimation Using Low-Cost MARG Sensors

This paper proposes a novel quaternion-based attitude estimator with magnetic, angular rate, and gravity (MARG) sensor arrays. A new structure of a fixed-gain complementary filter is designed fusing related sensors. To avoid using iterative algorithms, the accelerometer-based attitude determination is transformed into a linear system. Stable solution to this system is obtained via control theory. With only one matrix multiplication, the solution can be computed. Using the increment of the solution, we design a complementary filter that fuses gyroscope and accelerometer together. The proposed filter is fast, since it is free of iteration. We name the proposed filter the fast complementary filter (FCF). To decrease significant effects of unknown magnetic distortion imposing on the magnetometer, a stepwise filtering architecture is designed. The magnetic output is fused with the estimated gravity from gyroscope and accelerometer using a second complementary filter when there is no significant magnetic distortion. Several experiments are carried out on real hardware to show the performance and some comparisons. Results show that the proposed FCF can reach the accuracy of Kalman filter. It successfully finds a balance between estimation accuracy and time consumption. Compared with iterative methods, the proposed FCF has much less convergence speed. Besides, it is shown that the magnetic distortion would not affect the estimated Euler angles.

Estimation of Human Arm Joints Using Two Wireless Sensors in Robotic Rehabilitation Tasks.

This paper presents a novel kinematic reconstruction of the human arm chain with five degrees of freedom and the estimation of the shoulder location during rehabilitation therapy assisted by end-effector robotic devices. This algorithm is based on the pseudoinverse of the Jacobian through the acceleration of the upper arm, measured using an accelerometer, and the orientation of the shoulder, estimated with a magnetic angular rate and gravity (MARG) device. The results show a high accuracy in terms of arm joints and shoulder movement with respect to the real arm measured through an optoelectronic system. Furthermore, the range of motion (ROM) of 50 healthy subjects is studied from two different trials, one trying to avoid shoulder movements and the second one forcing them. Moreover, the shoulder movement in the second trial is also estimated accurately. Besides the fact that the posture of the patient can be corrected during the exercise, the therapist could use the presented algorithm as an objective assessment tool. In conclusion, the joints’ estimation enables a better adjustment of the therapy, taking into account the needs of the patient, and consequently, the arm motion improves faster.

Multi-sensor calibration of low-cost magnetic, angular rate and gravity systems.

We present a new calibration procedure for low-cost nine degrees-of-freedom (9DOF) magnetic, angular rate and gravity (MARG) sensor systems, which relies on a calibration cube, a reference table and a body sensor network (BSN). The 9DOF MARG sensor is part of our recently-developed “Integrated Posture and Activity Network by Medit Aachen” (IPANEMA) BSN. The advantage of this new approach is the use of the calibration cube, which allows for easy integration of two sensor nodes of the IPANEMA BSN. One 9DOF MARG sensor node is thereby used for calibration; the second 9DOF MARG sensor node is used for reference measurements. A novel algorithm uses these measurements to further improve the performance of the calibration procedure by processing arbitrarily-executed motions. In addition, the calibration routine can be used in an alignment procedure to minimize errors in the orientation between the 9DOF MARG sensor system and a motion capture inertial reference system. A two-stage experimental study is conducted to underline the performance of our calibration procedure. In both stages of the proposed calibration procedure, the BSN data, as well as reference tracking data are recorded. In the first stage, the mean values of all sensor outputs are determined as the absolute measurement offset to minimize integration errors in the derived movement model of the corresponding body segment. The second stage deals with the dynamic characteristics of the measurement system where the dynamic deviation of the sensor output compared to a reference system is corrected. In practical validation experiments, this procedure showed promising results with a maximum RMS error of .

Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs.

Orientation estimation using low cost sensors is an important task for Micro Aerial Vehicles (MAVs) in order to obtain a good feedback for the attitude controller. The challenges come from the low accuracy and noisy data of the MicroElectroMechanical System (MEMS) technology, which is the basis of modern, miniaturized inertial sensors. In this article, we describe a novel approach to obtain an estimation of the orientation in quaternion form from the observations of gravity and magnetic field. Our approach provides a quaternion estimation as the algebraic solution of a system from inertial/magnetic observations. We separate the problems of finding the “tilt” quaternion and the heading quaternion in two sub-parts of our system. This procedure is the key for avoiding the impact of the magnetic disturbances on the roll and pitch components of the orientation when the sensor is surrounded by unwanted magnetic flux. We demonstrate the validity of our method first analytically and then empirically using simulated data. We propose a novel complementary filter for MAVs that fuses together gyroscope data with accelerometer and magnetic field readings. The correction part of the filter is based on the method described above and works for both IMU (Inertial Measurement Unit) and MARG (Magnetic, Angular Rate, and Gravity) sensors. We evaluate the effectiveness of the filter and show that it significantly outperforms other common methods, using publicly available datasets with ground-truth data recorded during a real flight experiment of a micro quadrotor helicopter.

Motion Authoring for Mobile Avatars Using the Principles of Labanotation

An authoring method for a 3D (three-dimensional) avatar with complex DOFs (degrees of freedom) in mobile applications is proposed, in which an input device including MARG (magnetic, angular rate, and gravity) sensors is used. The user can perform predefined gestures by manually holding the sensor to define the mobile avatar’s desired motion, and the avatar performs the defined motion or poses that correspond to the predefined mapping, in real time. Because the DOFs of the avatar’s joints are far more than the DOFs of the MARG sensors, it is not easy to map the motion or poses of an avatar with the predefined sensor gestures. To alleviate this difficulty, a mechanism using the principles of Labanotation is suggested to control the motion of the avatar. Labanotation is a notation system for recording and analyzing human movement.

An Adaptive 6-DOF Tracking Method by Hybrid Sensing for Ultrasonic Endoscopes

In this paper, a novel hybrid sensing method for tracking an ultrasonic endoscope within the gastrointestinal (GI) track is presented, and the prototype of the tracking system is also developed. We implement 6-DOF localization by sensing integration and information fusion. On the hardware level, a tri-axis gyroscope and accelerometer, and a magnetic angular rate and gravity (MARG) sensor array are attached at the end of endoscopes, and three symmetric cylindrical coils are placed around patients' abdomens. On the algorithm level, an adaptive fast quaternion convergence (AFQC) algorithm is introduced to determine the orientation by fusing inertial/magnetic measurements, in which the effects of magnetic disturbance and acceleration are estimated to gain an adaptive convergence output. A simplified electro-magnetic tracking (SEMT) algorithm for dimensional position is also implemented, which can easily integrate the AFQC's results and magnetic measurements. Subsequently, the average position error is under 0.3 cm by reasonable setting, and the average orientation error is 1° without noise. If magnetic disturbance or acceleration exists, the average orientation error can be controlled to less than 3.5°.