Angular Rate Data Research Articles

From raw measurements to human pose - a dataset with low-cost and high-end inertial-magnetic sensor data

Wearable technology is expanding for motion monitoring. However, open challenges still limit its widespread use, especially in low-cost systems. Most solutions are either expensive commercial products or lower performance ad-hoc systems. Moreover, few datasets are available for the development of complete and general solutions. This work presents 2 datasets, with low-cost and high-end Magnetic, Angular Rate, and Gravity(MARG) sensor data. Provides data for the complete inertial pose pipeline analysis, starting from raw data, sensor-to-segment calibration, multi-sensor fusion, skeleton-kinematics, to complete Human pose. Contains data from 21 and 10 participants, respectively, performing 6 types of sequences, presenting high variability and complex dynamics with almost complete range-of-motion. Amounts to 3.5 M samples, synchronized with a ground-truth inertial motion capture system. Presents a method to evaluate data quality. This database may contribute to develop novel algorithms for each pipeline’s processing steps, with applications in inertial pose estimation algorithms, human movement forecasting, and motion assessment in industrial or rehabilitation settings. All data and code to process and analyze the complete pipeline is freely available.

Longitudinal Mode System Identification of an Insect-like Tailless Flapping-Wing Micro Air Vehicle Using Onboard Sensors

In this paper, model parameter identification results are presented for a longitudinal mode dynamic model of an insect-like tailless flapping-wing micro air vehicle (FWMAV) using angle and angular rate data from onboard sensors only. A gray box model approach with indirect method was utilized with adaptive Gauss–Newton, Levenberg–Marquardt, and gradient search identification methods. Regular and low-frequency reference commands were mainly used for identification since they gave higher fit percentages than irregular and high-frequency reference commands. Dynamic parameters obtained using three identification methods with two different datasets were similar to each other, indicating that the obtained dynamic model was sufficiently reliable. Most of the identified dynamic model parameters had similar values to the computationally obtained ones, except stability derivatives for pitching moment with forward velocity and pitching rate variations. Differences were mainly due to certain neglected body, nonlinear dynamics, and the shift of the center of gravity. Fit percentage of the identified dynamic model (~49%) was more than two-fold higher than that of the computationally obtained one (~22%). Frequency domain analysis showed that the identified model was much different from that of the computationally obtained one in the frequency range of 0.3 rad/s to 5 rad/s, which affected transient responses. Both dynamic models showed that the phase margin was very low, and that it should be increased by a feedback controller to have a robustly stable system. The stable dominant pole of the identified model had a higher magnitude which resulted in faster responses. The identified dynamic model exhibited much closer responses to experimental flight data in pitching motion than the computationally obtained dynamic model, demonstrating that the identified dynamic model could be used for the design of more effective pitch angle-stabilizing controllers.

TreeMMoSys: A low cost sensor network to measure wind-induced tree response

Severe storms caused the largest amount of damaged timber in European forests in the past 70 years. Storm damage occurs when wind loads exceed the failure limits of trees. A decisive factor in assessing storm damage is comprehensive knowledge of interactions between the aerial parts of trees and the high-impact airflow. This paper describes the inexpensive multiple sensor system TreeMMoSys that can measure aerial tree parts' wind-induced reactions, including branches and the stem. The output of TreeMMoSys includes acceleration and angular rate data converted to tilt angles in the post-processing. The system consists of a scalable number of light-weight tree response sensors and ground receivers that communicate through a WLAN network. The weatherproofed system is highly portable, reusable, and allows for an efficient monitoring and a maximization of the number of study trees. Due to the stable measurement performance and accuracy of TreeMMoSys, it can be deployed in the field for long-term monitoring of single tree reactions or neighboring trees' reactions to wind excitation.

Mechanisms and timing of injury to the thoracic, lumbar and sacral spine in simulated underbody blast PMHS impact tests

During an underbody blast (UBB) event, mounted occupants are exposed to high rate loading of the spine via the pelvis. The objective of this study was to simulate UBB loading conditions and examine mechanisms of injury in the thoracic, lumbar and sacral spine. Fourteen instrumented, whole-body, postmortem human subject (PMHS) experiments were performed using the WSU-decelerative horizontal sled system. The specimens were positioned supine on a decelerative sled, which then impacted an energy absorbing system mounted to a concrete barrier. Variables included the peak velocity and time-to-peak velocity for seat and floor, and the presence or absence of personal protective equipment (PPE) and seat padding. Post-test CT scans and autopsies were performed to identify the presence and severity of injuries. Acceleration and angular rate data collected at vertebra T1, T5, T8, T12, and S1 were used to assess injury timing and mechanisms. Additionally, joint time-frequency analysis (JTFA) of the spinal Z acceleration of the sacrum and vertebrae was developed with the aim of verifying spinal fracture timing. Injuries observed in the spine were attributed to axial compression applied through the pelvis, together with flexion moment due to the offset in the center of gravity of the torso, and are consistent with UBB-induced combat injuries reported in the literature. The injury timing estimation techniques discussed in this study provide a time interval when the fractures are predicted to have occurred. Furthermore, this approach serves as an alternative to the estimation methods using acoustic sensors, force and acceleration traces, and strain gauges.

Smart Phone Based Sensor Fusion by Using Madgwick Filter for 3D Indoor Navigation

In this paper, we proposed an enhanced pedestrian dead reckoning (PDR) system based on sensor fusion schemes using a smartphone. PDR is an effective technology for 3D indoor navigation. However, still, there are some obstacles to be overcome in its practical application. To track and simulate pedestrian’s position, which is confronted by environmental errors, walls, Bayesian errors, and other obstacles, our proposed PDR system enables estimation of stride based on the vertical accelerometer data and orientation from sensor fusion technique of magnetic angular rate and gravity sensor data by Madgwick filter. This localization system is independent of the received signal strength-based fingerprinting system. In addition, to estimate the current floor level, we make use of barometer information. To collect ground truth accurately and efficiently a prototype is implemented with the benchmark. We perform the same distance estimation for four different pedestrians to evaluate the accuracy of the proposed system. The real indoor experimental results demonstrate that the proposed system performs well while tracking the test subject in a 2D scenario with low estimation error (< 2 m). The 3D evaluation of the system inside a multi-story building shows that high accuracy can be achieved for a short range of time without position update from external sources. Then we compared localization performance between our proposed system and an existing (extended Kalman filter based) system.

Concurrent Detection of Linear and Angular Motion using a Single-Mass 6-axis Piezoelectric IMU

This paper exhibits operating system and performances of a novel single-mass 6-axis Inertial Measurement Unit (IMU) using piezoelectric detection. The electronic processing circuitry for the concurrent detection of linear and angular motion is proposed. The IMU structure is based on the use of 2 rings, connected with eight electrodes, implemented on the top of a piezoelectric membrane used for both sense and drive modes. The four inner electrodes are used for components detection due to the direct piezoelectric effect, while the outer electrodes are used to generate the drive mode due to the reverse piezoelectric effect. Through finite element analysis, we show that linear accelerations generate an offset voltage on the sensing electrodes, while angular rates lead to a change in the amplitude of the initial AC signal caused by the drive mode. The present work represents an innovative design able to separate 6 motion data from signals using only 4 electrodes. The specific electronic circuitry for acceleration and angular rate data dissociation shows a very efficient method for signal separation since no leakage readout occurs in all six axes. Besides, other particular interest is that under no circumstances, angular outputs disturb or affect acceleration ones and vice versa. The evaluated sensitivities are 364 mV/g and 65.5 mV/g for in-plane and out-of-plane linear accelerations, respectively. Similarly, angular rates sensitivities are 2.59 mV/rad/s and 522 mV/rad/s.

Reconstructing Vehicle Dynamics from On-Board Event Data

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Modern vehicles record dynamic data from a number of on-board sensors for events that could precede a crash. These data can be used to reconstruct the behavior of a vehicle, although the accuracy of these reconstructions has not yet been quantified. Here, we evaluated various methods of reconstructing the vehicle kinematics of a 2017 and a 2018 Toyota Corolla based on Vehicle Control History (VCH) data from overlapping events generated by the pre-collision system (PCS), sudden braking (SB) and anti-lock brake (ABS) activation. The vehicles were driven towards a stationary target at 32-64 km/h (20-40 mph) and then after the pre-collision alarm sounded the vehicle was steered sharply right or left and braked rapidly to rest. VCH data for PCS event were recorded at 2 Hz and for the sudden braking and ABS activation events at 6.7 Hz. The steering wheel angle and the vehicle’s longitudinal acceleration, lateral acceleration, and angular rate data were extracted and used to predict the vehicle position and heading over the duration of the VCH data record preceding the vehicle coming to rest. These predictions were generated by directly integrating the VCH data and by using the VCH data as inputs to PC-Crash simulations. The predicted positions and headings were then compared to the actual position and heading data measured using differential GPS synchronized to the VCH data record. The results of these analyses provide insights into the best methods for reconstructing vehicle kinematics from VCH data and estimates of the errors associated with different reconstruction techniques.&lt;/div&gt;&lt;/div&gt;

Estimation of Pedestrian Altitude Inside a Multi-Story Building Using an Integrated Micro-IMU and Barometer Device

This paper presents a novel method of estimating the altitude of pedestrians who are walking in the indoor environment of a multi-story building. We will show that to achieve pedestrian altitude estimation, a pedestrian only needs to wear a small MEMS-based integrated sensing device consisting of a micro-IMU (i.e., consists of a 3D accelerometer, a 3D gyroscope, and a 3D magnetometer) and a barometer, during indoor activities. High-precision estimates of the pedestrian's position in the vertical direction were obtained by utilizing the acceleration and angular rate data, as well as the height, deduced from barometer data. The inherent drifts of the IMU sensors, which lead to cumulative errors in altitude estimation, were sharply reduced using a complementary filter and an error compensation algorithm. The experimental results demonstrate that this method is effective in reducing estimation errors. When a person walks on stairs with the same step height, the error of the estimated height of each step is within 0.5 cm, and the cumulative height error is about 1.7% over a total height of 2.9 m. This integrated sensing device also exhibits good stability, i.e., three 20-min tests in a 12-h period showed that the cumulative error accounts for about 2% of the total height of 11.23 m. When the stairs have different heights, i.e., heights ranging from 12 to 28 cm, the estimated height error of each step is within 2 cm. With an ability to provide accurate and reliable vertical altitude estimates of pedestrians inside a multi-story building, the sensing device developed through this paper is suitable for use in 3D-space body tracking and pedestrian navigation applications.

Zero velocity interval detection based on a continuous hidden Markov model in micro inertial pedestrian navigation

Shoe-mounted pedestrian navigation systems based on micro inertial sensors rely on zero velocity updates to correct their positioning errors in time, which effectively makes determining the zero velocity interval play a key role during normal walking. However, as walking gaits are complicated, and vary from person to person, it is difficult to detect walking gaits with a fixed threshold method. This paper proposes a pedestrian gait classification method based on a hidden Markov model. Pedestrian gait data are collected with a micro inertial measurement unit installed at the instep. On the basis of analyzing the characteristics of the pedestrian walk, a single direction angular rate gyro output is used to classify gait features. The angular rate data are modeled into a univariate Gaussian mixture model with three components, and a four-state left–right continuous hidden Markov model (CHMM) is designed to classify the normal walking gait. The model parameters are trained and optimized using the Baum–Welch algorithm and then the sliding window Viterbi algorithm is used to decode the gait. Walking data are collected through eight subjects walking along the same route at three different speeds; the leave-one-subject-out cross validation method is conducted to test the model. Experimental results show that the proposed algorithm can accurately detect different walking gaits of zero velocity interval. The location experiment shows that the precision of CHMM-based pedestrian navigation improved by 40% when compared to the angular rate threshold method.

Probabilistic Context-Aware Step Length Estimation for Pedestrian Dead Reckoning

This paper introduces a weighted context-based step length estimation algorithm for pedestrian dead reckoning. Six pedestrian contexts are considered: stationary, walking, walking sideways, climbing and descending stairs, and running. Instead of computing the step length based on a single context, the step lengths computed for different contexts are weighted by the context probabilities. This provides more robust performance when the context is uncertain. The proposed step length estimation algorithm is part of a pedestrian dead reckoning system which includes the procedures of step detection and context classification. The step detection algorithm detects the step time boundaries using continuous wavelet transform analysis, while the context classification algorithm determines the pedestrian context probabilities using a relevance vector machine. In order to assess the performance of the pedestrian dead reckoning system, a data set of pedestrian activities and actions has been collected. Fifteen subjects have been equipped with a waist-belt smartphone and traveled along a predefined path. Acceleration, angular rate and magnetic field data were recorded. The results show that the traveled distance is more accurate using step lengths weighted by the context probabilities compared to using step lengths based on the highest probability context.

A Linear Kalman Filter for MARG Orientation Estimation Using the Algebraic Quaternion Algorithm

Real-time orientation estimation using low-cost inertial sensors is essential for all the applications where size and power consumption are critical constraints. Such applications include robotics, human motion analysis, and mobile devices. This paper presents a linear Kalman filter for magnetic angular rate and gravity sensors that processes angular rate, acceleration, and magnetic field data to obtain an estimation of the orientation in quaternion representation. Acceleration and magnetic field observations are preprocessed through a novel external algorithm, which computes the quaternion orientation as the composition of two algebraic quaternions. The decoupled nature of the two quaternions makes the roll and pitch components of the orientation immune to magnetic disturbances. The external algorithm reduces the complexity of the filter, making the measurement equations linear. Real-time implementation and the test results of the Kalman filter are presented and compared against a typical quaternion-based extended Kalman filter and a constant gain filter based on the gradient-descent algorithm.

Design and Development of an Instrumented Drifter for Lagrangian Measurements of Inertial Particle Dynamics in Breaking Waves

Understanding wave breaking phenomena is a challenging problem that still lacks satisfactory understanding. The present paper is concerned with the development of novel instrumentation adapted to obtain Lagrangian field measurements of essential variables that are intimately related to the physics of wave breaking. These miniature (6.4 cm) Lagrangian drifters are equipped with micro-electromechanical systems (MEMS) inertial measurement units (IMUs) that record acceleration, angular rate and magnetic field data at rapid sampling rates (100 Hz). Using a quaternion-based sensor fusion algorithm, multiple sensor data is filtered in postprocessing procedures to obtain the best possible estimates of particle orientation and acceleration. We show that the developed instrumentation is able to collect meaningful data of inertial particle dynamics in a controlled laboratory flow, where the instrumented particle trajectories are benchmarked against an analytical representation of the flow. Results indicate that dead reckoning errors grow quadratically in time and that for a typical wave overturning event trajectory estimates are expected to remain within a radius $r 0.7 m with a 95% confidence level. Also, special attention is given to gyroscope reliability during the actual wave breaking process through an empirical evaluation of gyroscope performance in plunging waves. Angular rate measurements from two separate gyroscopes mounted inside the same drifter show high agreement leading to the conclusion that gyroscope errors arising from wave induced vibrations are negligible. Finally, we report on the first ever Lagrangian field observations of inertial particle dynamics in plunging waves with heights on the order of 2 m. We expect that further development of the technology and analysis tools presented herein could revolutionize our understanding of the hydrodynamics of breaking waves.

A self-developed indoor three-dimensional pedestrian localization platform based on MEMS sensors

Purpose – This study aims to design a new low-cost localization platform for estimating the location and orientation of a pedestrian in a building. The micro-electro-mechanical systems (MEMS) sensor error compensation and the algorithm were improved to realize the localization and altitude accuracy. Design/methodology/approach – The platform hardware was designed with common low-performance and inexpensive MEMS sensors, and with a barometric altimeter employed to augment altitude measurement. The inertial navigation system (INS) – extended Kalman filter (EKF) – zero-velocity updating (ZUPT) (INS-EKF-ZUPT [IEZ])-extended methods and pedestrian dead reckoning (PDR) (IEZ + PDR) algorithm were modified and improved with altitude determined by acceleration integration height and pressure altitude. The “AND” logic with acceleration and angular rate data were presented to update the stance phases. Findings – The new platform was tested in real three-dimensional (3D) in-building scenarios, achieved with position errors below 0.5 m for 50-m-long route in corridor and below 0.1 m on stairs. The algorithm is robust enough for both the walking motion and the fast dynamic motion. Originality/value – The paper presents a new self-developed, integrated platform. The IEZ-extended methods, the modified PDR (IEZ + PDR) algorithm and “AND” logic with acceleration and angular rate data can improve the high localization and altitude accuracy. It is a great support for the increasing 3D location demand in indoor cases for universal application with ordinary sensors.

Adaptive data filtering of inertial sensors with variable bandwidth.

MEMS (micro-electro-mechanical system)-based inertial sensors, i.e., accelerometers and angular rate sensors, are commonly used as a cost-effective solution for the purposes of navigation in a broad spectrum of terrestrial and aerospace applications. These tri-axial inertial sensors form an inertial measurement unit (IMU), which is a core unit of navigation systems. Even if MEMS sensors have an advantage in their size, cost, weight and power consumption, they suffer from bias instability, noisy output and insufficient resolution. Furthermore, the sensor's behavior can be significantly affected by strong vibration when it operates in harsh environments. All of these constitute conditions require treatment through data processing. As long as the navigation solution is primarily based on using only inertial data, this paper proposes a novel concept in adaptive data pre-processing by using a variable bandwidth filtering. This approach utilizes sinusoidal estimation to continuously adapt the filtering bandwidth of the accelerometer's data in order to reduce the effects of vibration and sensor noise before attitude estimation is processed. Low frequency vibration generally limits the conditions under which the accelerometers can be used to aid the attitude estimation process, which is primarily based on angular rate data and, thus, decreases its accuracy. In contrast, the proposed pre-processing technique enables using accelerometers as an aiding source by effective data smoothing, even when they are affected by low frequency vibration. Verification of the proposed concept is performed on simulation and real-flight data obtained on an ultra-light aircraft. The results of both types of experiments confirm the suitability of the concept for inertial data pre-processing.

Integrated SVD/EKF for Small Satellite Attitude Determination and Rate Gyro Bias Estimation

In this study an integrated singular value decomposition (SVD)/ Extended Kalman filter (EKF) attitude determination system is presented, in which the SVD and EKF algorithms are combined to estimate the attitude angles and angular velocities and gyro biases, respectively. As a reference directions for SVD, the unit vectors toward the Sun and Earth's Magnetic Field are used. The Euler angles produced SVD are provided as input to the EKF along with angular rate data. The parameters of satellite's rotational motion and rate gyro biases are estimated using EKF. In comparison to more traditional approaches, this preprocessing step significantly reduces the complexity of filter design by allowing the use of linear measurement equations.

Attitude Determination by Integration of Multi-Sensors Based on Federated Kalman Filter

Micro inertial measurement unit integration storage test system is a typical multi-sensor information fusion system consists of microsensors. The Federated Kalman filter is applied to micro inertial measurement unit integration storage test system. The general structure and characteristics of Federated Kalman filter is expounded. The four-order Runge-Kutta method based on quaternion differential equation was used to dispose the output angular rate data from gyroscope, and the recurrence expressions was established too. The control system based ARM Cortex-M4 master-slave structure is adopted in this paper. The result shown that the dimensionality reduced algorithm significantly reduces implementation complexity of the method and the amount computation. The filtering effect and real-time performance have much increased than traditionally method.

Telemonitoring of patients with Parkinson's disease using inertia sensors.

Medical treatment in patients suffering from Parkinson's disease is very difficult as dose-finding is mainly based on selective and subjective impressions by the physician. To allow for the objective evaluation of patients' symptoms required for optimal dosefinding, a telemonitoring system tracks the motion of patients in their surroundings. The system focuses on providing interoperability and usability in order to ensure high acceptance. Patients wear inertia sensors and perform standardized motor tasks. Data are recorded, processed and then presented to the physician in a 3D animated form. In addition, the same data is rated based on the UPDRS score. Interoperability is realized by developing the system in compliance with the recommendations of the Continua Health Alliance. Detailed requirements analysis and continuous collaboration with respective user groups help to achieve high usability. A sensor platform was developed that is capable of measuring acceleration and angular rate of motions as well as the absolute orientation of the device itself through an included compass sensor. The system architecture was designed and required infrastructure, and essential parts of the communication between the system components were implemented following Continua guidelines. Moreover, preliminary data analysis based on three-dimensional acceleration and angular rate data could be established. A prototype system for the telemonitoring of Parkinson's disease patients was successfully developed. The developed sensor platform fully satisfies the needs of monitoring patients of Parkinson's disease and is comparable to other sensor platforms, although these sensor platforms have yet to be tested rigorously against each other. Suitable approaches to provide interoperability and usability were identified and realized and remain to be tested in the field.

Application of a Single-Antenna GPS–Based Attitude Estimation on the Stability Control of a Small Unmanned Aerial Vehicle

AbstractAttitude estimation is an important issue in the autonomous flight of an unmanned aerial vehicle (UAV) system. However, for a micro-sized UAV with a wingspan of less than 2 m, a high performance Attitude and Heading Reference System (AHRS) is usually too heavy and large, whereas a low-cost AHRS of suitable size and weight may easily give erroneous attitude data. In this study, a single-antenna global positioning system (GPS)–based attitude estimation method, called pseudoattitude, in combination with the angular rate data given by the onboard gyroscopes, is applied to solve the problem. That is, the pseudoattitude estimated by the GPS velocity data provides a reference attitude with some time lag, whereas the gyroscope’s angular rate is integrated with the aid of a proposed complementary filter to eliminate the time lag problem. An experimental method is then developed to determine the filter parameters by a static ground test of the GPS and flight test data with postprocessing and analysis. A fuz...

Poster 61 Using Inertial Sensor Based Novel System to Assess Upper Limb Movement in Brain Injury

To use inertial motion sensors to develop a novel, precise, non-invasive and objective assessment tool to detect the trajectory of the upper limb movement of patients with brain injury. Initially high quality commercial sensors (Xsens MTx) which are the gold standard for inertial measurements are used. These provided 3-axis acceleration data, 3-axis angular rate (angular velocity) data and 3-axis orientation data. The feasibility of utilizing low cost inertial sensors used in gaming (e.g., Wii) was evaluated on volunteers as per Ethics committee agreement. Subsequently, the system was used with the patients with brain injury. In-patient neuro-rehabilitation unit of a regional UK teaching hospital. Four volunteers and four patients with brain injury. Nine Hole Peg Test, Kinematics data. It is possible to monitor the motion of upper limb joints with a drift no greater than 0.5cm and segment rotation angles to within one degree over a measurement period of 25 seconds. Sensor data also provided timing information. Utilizing the kinematics model and inertial sensors has enabled the measurement of acceleration, velocity and movement profiles of the shoulder, elbow and wrist joints/segments. The information profile was richer than usual clinical test. The timing data from nine-hole peg tests on volunteers has already identified a control mechanism that seeks to place each peg in each hole in the same time, irrespective of distance travelled. Whether the same holds true for patients with upper limb dysfunction or, a sign of recovery will be investigated even with specific interventions (e.g., Botox).

Feasibility Study and Performance Analysis of a Gyroless Orientation Tracker

Inertial orientation tracking systems commonly use three types of sensors: accelerometers, magnetometers, and gyroscopes. The angular rate signal is used to obtain a dead reckoning estimate, whereas the gravitational and local magnetic field measures allow us to apply a correction and to obtain a drift-free result. Considering the present market of inertial MEMS sensors, the current consumption of gyroscopes represents a major part of the power budget of wireless inertial sensor nodes, which should be minimized given the mobility of the application. This paper introduces an orientation tracking algorithm, based on an unscented Kalman filter, that does not require angular rate data for tracking human movements up to 450 °/s , which is a reasonable value for many applications. Since accelerometers measure other accelerations beside gravity and magnetometers are prone to magnetic disturbances, adaptive techniques are applied in order to reduce the influence on the estimations. The performance of the system is quantitatively analyzed and compared to an estimator that includes angular rate information.