Gravity Sensor Research Articles

A Testing Method for Shipborne Atomic Gravimeter Based on the Modulated Coriolis Effect

Shipborne atomic gravimeter (SAG) is an instrument that can directly measure absolute gravity in dynamic environments. As a new type of gravity sensor, a standard method for evaluating its detailed performance has not been proposed and the detailed performance of SAG was rarely reported. In this paper, a system of dynamic gravity measurement, which was integrated with a home-made atomic gravimeter, is demonstrated, and a novel and simple method for testing the performance of SAG on the lake based on the modulated Coriolis effect is put forward. Firstly, in the state of ship mooring, a tilt modulation of the gravity sensor has been realized to make sure the Raman wave vector is parallel to the gravity axis. Moreover, a comparison between the measurement result of CG-5 and SAG has also been carried out to evaluate the accuracy of the SAG. Then, the Coriolis effect modulating experiment is carried out with various routes on lake to test its performance in dynamic environments. In the ship mooring state, the accuracy has been demonstrated to be 0.643 mGal. The internal consistency reliabilities are evaluated to be 0.8 mGal and 1.2 mGal under the conditions of straight line and circle navigation, respectively.

An Observation Model From Linear Interpolation for Quaternion-Based Attitude Estimation

This article aims to solve multiple problems associated with attitude estimation; the complexity of Kalman filter (KF) calculations in the attitude and heading reference system (AHRS), the interference sensitivity of magnetic, angular rate, and gravity (MARG) sensors, and the low accuracy of the factored quaternion algorithm (FQA). It presents a two-layer linear KF using MARG sensors to obtain attitude estimation in quaternions. First, data from a triaxial accelerometer and magnetometer is processed by a novel algorithm, which fuses the quaternion estimator (QUEST) algorithm and FQA by the linear interpolation (LERP) to obtain an observation model. Second, the process model in the two-layer KF was obtained by using LERP to fuse an optimum quaternion obtained from a gyroscope and FQA. The LERP can eliminate gyro bias drift, and integral error, and compensate for unexpected conditions, such as fast rotation and temporary strong magnetic disturbances. The proposed algorithm presents higher accuracy and lower computational load than QUEST or FQA used with a KF alone. The performance of the proposed algorithm is verified through simulation and experiments.

Device Attitude and Real-Time 3D Visualization: An Interface for Elderly Care

Objectivethis paper presents an innovative graphical user interface to visualize the attitude of a sensing device in a three-dimensional space, serving a wide-range of medical applications. Material and methodsbased on inertial measurement units (IMU) or on magnetic, angular rate and gravity (MARG) sensors, a processing unit provides Euler angles using a sensor fusion technique to display the orientation of the device relative to the Earth frame in real-time. The device is schematized by linking six polygonal regions, and is subject to sequential rotations by updating the graph each 350 ms. We conduct comparative studies between the two sensing devices, i.e. IMUs and MARGs, as well as two orientation filters, namely Madgwick's algorithm and Mahony's algorithm. Resultsthe accuracy of the system is reported as a function of (i) the sampling frequency, (ii) the sensing unit, and (iii) the orientation filter, following two elderly care applications, namely fall risk assessment and body posture monitoring. The experiments are conducted using public datasets. The corresponding results show that Madgwick's algorithm is best suited for low sampling rates, whereas MARG sensors are best suited for the detection of postural transitions. Conclusionthis paper addresses the different aspects and discusses the limitations of attitude estimation systems, which are important tools to help clinicians in their diagnosis.

Automated documentation of almost identical movements in the context of dementia diagnostics

When monitoring the health state of people with neurological diseases, it is crucial to detect disease progression and disease-related changes as soon as possible. Such changes can be detected in the performance of everyday activities like eating, drinking, nose blowing, reading, looking at photos, knitting, telephoning, or brushing hair. In order to supervise such everyday activities, this work proposes an approach employing smartwatches in combination with a Recurrent neural network. A smartwatch offers the possibility of integrating sensor technology into a patient’s daily routines in a unobtrusive way. We have developed a machine learning web tool to track motion data using smartwatch sensors. Data from the accelerometer, heart rate sensor, gyroscope, gravity sensor and position sensor are tracked at 20 Hz. Our tool allows a systematic comparison of the performance between a Long Short-Term Memory (LSTM) model and the different sensor systems in classifying twelve nearly identical daily activities. In this paper, we address the problem of caregiver effort in creating daily care documentation for dementia patients. We present an activity classification system that enables automatic classification of daily activities of patients with dementia across a nursing shift. To assist caregivers, we provide an idea for integrating our system into a nursing documentation. • When monitoring the health state of people with neurological diseases, it is crucial to detect disease progression and disease-related changes as soon as possible. Such changes can be detected in the performance of everyday activities like eating, drinking, nose blowing, reading, looking at photos, knitting, telephoning, or brushing hair. • In order to supervise such everyday activities, this work proposes an approach employing smartwatches in combination with a Recurrent neural network. A smartwatch offers the possibility of integrating sensor technology into a patient’s daily routines in a unobtrusive way. • We have developed a machine learning web tool to track motion data using smartwatch sensors. Data from the accelerometer, heart rate sensor, gyroscope, gravity sensor and position sensor are tracked at 20 Hz. • Our tool allows a systematic comparison of the performance between a Long Short-Term Memory (LSTM) model and the different sensor systems in classifying twelve nearly identical daily activities. • In this paper, we address the problem of caregiver effort in creating daily care documentation for dementia patients. We present an activity classification system that enables automatic classification of daily activities of patients with dementia across a nursing shift. To assist caregivers, we provide an idea for integrating our system into a nursing documentation.

DeepVIP: Deep Learning-Based Vehicle Indoor Positioning Using Smartphones

The advent of sensor-rich smart devices (e.g., smartphones) has enabled a lot of applications and services. One of these applications and services is smartphone-based vehicle indoor positioning, which is a key technology for smart car parking and driverless cars. So far, most vehicle indoor positioning solutions either use infrastructures (e.g., WiFi access points) or inertial sensors, which suffer from low positioning accuracy, limited coverage, or high cost to deploy new equipment. To tackle these challenges, in this work we propose a novel Deep Learning-based Vehicle Indoor Positioning (DeepVIP) approach using smartphone built-in sensors, including accelerometer, gyroscope, magnetometer, and gravity sensor. Experiments are conducted in indoor parking areas. Experimental results show that the proposed method outperforms the state-of-the-art methods.

Online calibrated, energy-aware and heading corrected pedestrian navigation with foot-mounted MARG sensors

The objective of this paper is to propose an energy-aware and gait corrected pedestrian dead reckoning (PDR) approach using foot-mounted magnetic, angular rate and gravity (MARG) sensors. Compared with existing algorithms of PDR, the proposed method aims to solve three main problems for real pedestrian applications. First, to avoid limitations of off-line calibration for personal step length parameters, we utilize the zero-velocity-update (ZUPT) aided pedestrian MARG performance to continuously compute one’s pose information. Meanwhile, it accumulates the moving distance for further estimation of one’s step length during the initialization process. Secondly, due to different pedestrian gaits implicating the heading deviation angle between one’s moving direction and heading, there are non-negligible impacts on pedestrian dead reckoning accuracy. The linear Kalman filter (KF) is used to recursively estimate the deviated heading during aforementioned initialization process. The third problem is related to the energy consumption. Following three aspects of adaptive energy saving work are devised: (i) energy-aware strategy for gyroscopes measurements acquisition is adopted to guarantee lower energy consumption. (ii) Modes switching mechanism of navigation computation is applied to the initialization and dead reckoning processes. (iii) De-sampling after initialization process has been invoked. Finally, real-world experiments are carried out to evaluate the performances of developed PDR system. The results show the efficiency of the suggested approach.

Accelerometery-Based Load Symmetry in Track Running Kinematics concerning Body Location, Track Segment, and Distance in Amateur Runners

Background: Previous studies indicate that running at maximum speed on short or curved sections is slower than running on straight sections. This study aimed to analyse the external load symmetry in track running kinematics concerning body location (left vs. right, caudal vs. cephalic), track segment (straight vs. curved) and distance (150 m vs. 300 m). Methods: Twenty experienced athletes ran 150 m and 300 m on an official athletic track and were monitored by Magnetic, Angular Rate and Gravity sensors attached to six different body segments (thorax, lumbar, knees and malleolus). Player Load was quantified as a valid, effective and representative Accelerometery-based variable. Results: (1) Principal component analysis explained 62–93% of the total variance and clustered body locations relevance in curved (knees and malleolus) vs. straight (lumbar, knees, malleolus) running segments; (2) Player Load statistical differences by track segment (curved vs. straight) were found in all body locations; and (3) there were no differences in bilateral symmetries by distance or running segment. Conclusions: Track segment and body location directly impacted accelerometery-based load. Acceleration in straight segments was lower compared to that in curved segments in all the body locations (lumbar, knee and ankle), except in the thorax. Strength and conditioning programs should consider the singularity of curved sprinting (effects of centripetal–centrifugal force) for performance enhancement and injury prevention and focus on the knees and malleolus, as shown in the principal component analysis results.

Professional Ballet Dancers’ Balance Ability and Psychological Traits and States: A Longitudinal Pilot Study

Introduction:Professional dancers are committed to dancing despite their injuries and mood changes. Therefore, dancers’ balance ability should be evaluated as an objective indicator of the quality of their dance/dancing ability.Objective:We aimed to examine the relationship between the balance ability of professional ballet dancers and their psychological traits and states by serotonin polymorphism, using pressure and center of gravity sensors.Methods:Five female professional dancers participated in this three-month longitudinal pilot study. The dancers’ stress status was assessed each month on three conditional days to gauge stress conditions. Before and after their standard warm-up exercises, the dancers’ center of gravity was assessed while standing and posing in ballet-specific positions, and psychological questionnaire surveys were conducted to assess the level of anxiety and objective stress. After all observations were completed, serotonin polymorphism was analyzed as one of the psychological traits.Results:All participants possessed the s/s-type (short type) serotonin transporter gene promoter polymorphism, indicating high sensitivity to stress. Their average trait anxiety score was 50.80±8.47. However, all participants were able to be calm, had no injuries, and maintained a stable center of gravity throughout the study period.Conclusion:Throughout the study, from ordinary practice to the time of the approaching ballet performance, the participants maintained stable balance ability regardless of their psychological traits and states of mind. It is unlikely that reactions to stress and psychological traits affected professional dancers’ physicality. Thus, continuous assessment of the balance between mental and physical aspects enhances performance management.

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.

Upper limb orientation assessment as an articulated body chain.

Upper limb orientation estimation based on Magnetic, Angular Rate, and Gravity sensors considering multiple body segments is presented in this work. The proposal allowed assessing the activity of two or more body segments individually and jointly, regardless of their spatial relationship. A custom-made system was developed incorporating a complementary filter and a proportional-integral control for data sensor merging and, noise and instrumentation error reduction. Two controlled tests were carried out to assess the performance of the system. The former evaluated the response of the method in motionless conditions, while the latter assessed the feasibility to follow trajectories in 3D space. Ten volunteers were recruited to evaluate the system performance in three semi-controlled and daily life task tests. The system was evaluated using the common parameters in motion tracking methods and relied on a digital motion processor. The system's outcome presented a root mean square error in the range of 2.65°-3.98° for the semi-controlled tests and 0.48°-1.389° for the daily life task test. The system tests analysis proved that the proposal permitted obtaining the articulated body chain information of multiple segments when three or more MARG sensors are used.

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.

Research on Gradient-Descent Extended Kalman Attitude Estimation Method for Low-Cost MARG.

Aiming at the problem of the weak dynamic performance of the gradient descent method in the attitude and heading reference system, the susceptibility to the interference of accelerometers and magnetometers, and the complex calculation of the nonlinear Kalman Filter method, an extended Kalman filter suitable for a low-cost magnetic, angular rate, and gravity (MARG) sensor system is proposed. The method proposed in this paper is a combination of a two-stage gradient descent algorithm and the extended Kalman filter (GDEKF). First, the accelerometer and magnetometer are used to correct the attitude angle according to the two-stage gradient descent algorithm. The obtained attitude quaternion is combined with the gyroscope measurement value as the observation vector of EKF and the calculated attitude of the gyroscope and the bias of the gyroscope are corrected. The elimination of the bias of the gyroscope can further improve the stability of the attitude observation results. Finally, the MARG sensor system was designed for mathematical model simulation and hardware-in-the-loop simulation to verify the performance of the filter. The results show that compared with the gradient descent method, it has better anti-interference performance and dynamic performance, and better measurement accuracy than the extended Kalman filter.

Gravity sensor employs atoms' quantum behavior to see underground

Gravity sensor employs atoms' quantum behavior to see underground

Ocular counter-roll is less affected in experienced versus novice space crew after long-duration spaceflight

Otoliths are the primary gravity sensors of the vestibular system and are responsible for the ocular counter-roll (OCR). This compensatory eye torsion ensures gaze stabilization and is sensitive to a head roll with respect to gravity and the Gravito-Inertial Acceleration vector during, e.g., centrifugation. To measure the effect of prolonged spaceflight on the otoliths, we quantified the OCR induced by off-axis centrifugation in a group of 27 cosmonauts in an upright position before and after their 6-month space mission to the International Space Station. We observed a significant decrease in OCR early postflight, larger for first-time compared to experienced flyers. We also found a significantly larger torsion for the inner eye, the eye closest to the rotation axis. Our results suggest that experienced cosmonauts have acquired the ability to adapt faster after G-transitions. These data provide a scientific basis for sending experienced cosmonauts on challenging missions that include multiple g-level transitions.

Separating the output ports of a Bragg interferometer via velocity selective transport

We report on the study of a detection scheme based on a Bloch separator in two-photon Bragg interferometers. We increase the spatial separation between the two output ports of the interferometer by selectively imparting 30 Bloch oscillations to one of them before their detection via time of flight. This method allows increasing the duration of the interferometer by reducing the time for discriminating the ports of the interferometer at detection. We study in detail the impact of this separator on the performance of a dual gravity sensor, and in particular on its measurement sensitivities to gravity acceleration and gravity gradients.

Residual neuromuscular fatigue influences subsequent on-court activity in basketball

ABSTRACT The aim of this study was to determine if residual neuromuscular fatigue influenced subsequent match and training activity in professional women’s basketball. Prior to matches and training sessions throughout a season, players performed countermovement jumps while wearing a magnetic, angular rate and gravity (acceleration) sensor on their upper back. Flight time to contraction time ratio was used to determine neuromuscular performance and to identify neuromuscular fatigue. Average session intensity and volume, the proportion of live time spent in different intensity bands (matches), and absolute and relative time spent in different intensity bands (training) were quantified using accelerometry. Residual neuromuscular fatigue was deemed to be present when the decrement in neuromuscular performance relative to pre-season baseline was greater than the smallest worthwhile change. Players displayed residual neuromuscular fatigue before 16% of matches and 33% of training sessions. When players were fatigued prior to matches, the proportion of live time undertaking supramaximal activity was 5.7% less (p = 0.02) and moderate-vigorous activity was 3.7% more than when not fatigued (p = 0.02). When fatigued prior to training, the players displayed a 2.6% decrement in average intensity (p = 0.02), 2.8% decrement in absolute (p = 0.01) and 5.0% decrement in relative (p = 0.01) maximal activity, as well as 13.3% decrement in absolute (p < 0.01) and 6.8% decrement in relative (p < 0.01) supramaximal activity when compared to not being fatigued. These findings suggest that residual neuromuscular fatigue influences players’ ability to perform supramaximal activity, which highlights the importance of monitoring neuromuscular performance throughout a professional season. Highlights Residual neuromuscular fatigue can influence the amount of supramaximal activity players perform in a subsequent training session or match. Practices should be implemented to minimise residual neuromuscular fatigue carried into matches while maintaining a sufficient training volume to elicit physiological adaptations. MARG sensors can be used as an affordable and time-efficient tool for regularly monitoring countermovement jump-derived neuromuscular fatigue.

Effects of vertical nonlinearity on the superconducting gravimeter CT #036 at Ishigakijima, Japan

One of the characteristic features of the gravity recordings produced by the superconducting gravimeter CT #036 at Ishigakijima, Japan, is that it indicates gravity increase when a typhoon (hurricane) approaches the island. Since we are trying to detect small gravity signals associated with the long-term slow slip events in this region, it is very important in the interpretation of the observed data whether such gravity changes are of natural or instrumental origin. In this paper, we investigate whether or not nonlinearity in the sensor of the superconducting gravimeter is responsible for this phenomenon. Here we take the same theoretical approach as taken by our previous study which investigated the effect of coupling between horizontal and vertical components of the gravity sensor in order to understand the noise caused by the movements of a nearby VLBI antenna. From theoretical and experimental approaches, we prove that the gravity increase observed by CT #036 at the times of high background noise level cannot be explained by instrumental effects, such as the nonlinearity in the vertical component or the coupling between horizontal and vertical components of the gravity sensor. This implies that the observed gravity increases are real gravity signals of natural origin.Graphical

Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models

Abstract. Parametric geological models such as implicit or kinematic models provide low-dimensional, interpretable representations of 3-D geological structures. Combining these models with geophysical data in a probabilistic joint inversion framework provides an opportunity to directly quantify uncertainty in geological interpretations. For best results, care must be taken with the intermediate step of rendering parametric geology in a finite-resolution discrete basis for the geophysical calculation. Calculating geophysics from naively voxelized geology, as exported from commonly used geological modeling tools, can produce a poor approximation to the true likelihood, degrading posterior inference for structural parameters. We develop a simple integrated Bayesian inversion code, called Blockworlds, showcasing a numerical scheme to calculate anti-aliased rock properties over regular meshes for use with gravity and magnetic sensors. We use Blockworlds to demonstrate anti-aliasing in the context of an implicit model with kinematic action for simple tectonic histories, showing its impact on the structure of the likelihood for gravity anomaly.

Mobile behavioral biometrics for passive authentication

• We present a first benchmark of HuMIdb, a publicly available HCI database. • We evaluate touchscreen and background sensor data for mobile passive authentication. • We train a recurrent neural network with triplet loss for each individual modality. • A multimodal system is achieved through the fusion of modalities at score level. • Our results show the suitability of behavioral biometrics for mobile authentication. Current mobile user authentication systems based on PIN codes, fingerprint, and face recognition have several shortcomings. Such limitations have been addressed in the literature by exploring the feasibility of passive authentication on mobile devices through behavioral biometrics. In this line of research, this work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits acquired while the subjects perform different activities on the phone such as typing, scrolling, drawing a number, and tapping on the screen, considering the touchscreen and the simultaneous background sensor data (accelerometer, gravity sensor, gyroscope, linear accelerometer, and magnetometer). Our experiments are performed over HuMIdb, 1 1 https://github.com/BiDAlab/HuMIdb one of the largest and most comprehensive freely available mobile user interaction databases to date. A separate Recurrent Neural Network (RNN) with triplet loss is implemented for each single modality. Then, the weighted fusion of the different modalities is carried out at score level. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke in a fixed-text scenario. In all cases, the fusion of modalities is very beneficial, leading to Equal Error Rates (EER) ranging from 4% to 9% depending on the modality combination in a 3-second interval.

The Construction of Online Course Learning Model of Ideological and Political Education for College Students from the Perspective of Machine Learning

Student behaviour analysis in the classroom is an important part of teaching and educational innovations that can assist the institution find an effective strategy to improve students’ learning efficiency and ability to innovate. In this study, a human behavior recognition system is proposed for monitoring the learning status of students in the course of ideological and political education using the signals of smartphone embedded gravity sensors. A convolution neural network (CNN) is used to automatically extract prominent patterns from the raw signals of smartphone embedded sensors followed by the classification of the seven student activities including walking, going upstairs, downstairs, lying, sitting, standing, and running, respectively. The optimized CNN model was obtained after training on 1,500 training samples of student’s behavior data. The model is evaluated in terms of evaluation metrics such as accuracy, precision, and recall. The proposed model achieved 97.83% accuracy, 97.82% precision, and 97.83% recall, respectively, which are significantly higher than the classification performance of the other recognition models. The proposed model achieved inspiring performance compared to the existing behavior recognition systems. The model of human behavior can obtain the learning state behavior of the students from the college students’ listening equipment, to understand the learning situation of the students.