Gravity Sensor Research Articles

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.

Improved attitude estimation accuracy by data fusion of a MEMS MARG sensor and a low-cost GNSS receiver

To obtain precise and continuous attitude information, this paper proposed a data fusion scheme that exploits the complementary advantages of a microelectromechanical system (MEMS) magnetic, angular rate, and gravity (MARG) sensor and a low-cost global navigation satellite system (GNSS) receiver. A quaternion-based error state Kalman filter is designed to integrate the different data sources, where the attitude error and gyro bias variation are taken as the error states. The gyro-measured angular rates are continuously integrated to propagate the attitude quaternion, while the accelerometer and magnetometer readings or the GNSS pitch and heading angles estimated by the precise carrier phase observations are utilized to perform the measurement update. The system kinematic model remains constant when the aiding source changes, only the measurement model alternates accordingly. A vehicular dynamical test was conducted to evaluate the proposed algorithm, the test results show that the attitude estimation accuracy of the integrated algorithm is apparently improved.

A Computationally Efficient Outlier-Robust Cubature Kalman Filter for Underwater Gravity Matching Navigation

Gravity-aided navigation is one of key techniques for the navigation of underwater vehicles. Cubature Kalman filter (CKF)-based matching algorithm improves the positioning accuracy of traditional Sandia inertial terrain aided navigation method. However, the gravity sensor may suffer from outlier interferences due to complex and changeable underwater environments, which degrades the performance of CKF-based matching algorithm remarkably. To address this problem, in this article, a novel computationally efficient outlier-robust CKF-based matching algorithm is proposed for an underwater gravity-aided navigation system with outlier-contaminated measurements. The convergence analysis and the stability discussions are given to show the effectiveness of the proposed algorithm, and the discussion on computational complexity illustrates the good real-time performance of the proposed algorithm. Simulation and experimental results demonstrate the advantages of the proposed matching algorithm as compared with existing state-of-the-art matching algorithms.

Design and Construction of a Superconducting Gravimeter Prototype

To promote China’s research on fields related to high-accuracy gravity measurement, the Institute of Electrical Engineering, Chinese Academy of Sciences (IEECAS), is committed to developing superconducting gravimeters. A superconducting gravimeter prototype has been successfully designed, fabricated, and tested through continuous improvement and optimization design. This article describes the main components of the prototype: superconducting gravity sensor, magnetic shield, cryogenic system, and displacement detection and feedback system. The novel work improves the superconducting gravity sensor on the persistent current switch and superconducting joint. Furthermore, the method for testing the levitation stiffness is also introduced, and the result demonstrates that an electromagnetic force method is a feasible approach with fast and good linearity. Finally, experimental test results on the prototype are shown, where solid Earth tide, temperature effect, and parasitic mode are obtained in the prototype signal. Moreover, it is shown that the levitation stiffness obtained by the electromagnetic force method and the tidal observation method are close to each other.

A model of the response of the MGS-6 gravity sensor to tilting

The reliable interpretation of the measurements made by the Micro-g marine gravimetric system (MGS-6) depends on how the temporary changes of the scale coefficients such as gravimeter scale factor, vertical cross-coupling (VCC) effect, tiltmeter cross and tiltmeter long are compensated for during the signal analysis. The listed coefficients cannot be determined from readings during the measurements or by analysing the final data. The method presented here can be used to periodically check individual scale factors before starting shipborne measurements. This article focuses on determining the scale coefficients of the gravimeter: VCC effect, tiltmeter cross and tiltmeter long based on the MGS-6 gravity sensor's response to tilt. An unique non-linear model of Lacoste & Romberg gravimeter response to tilt was developed. In this paper, the measurement of the tilt angle of the object based on the photogrammetric elaboration of metric photographs is presented, using the principles of one-image photogrammetry.

Smartphone-Based Pedestrian Dead Reckoning for 3D Indoor Positioning.

Indoor localization based on pedestrian dead reckoning (PDR) is drawing more and more attention of researchers in location-based services (LBS). The demand for indoor localization has grown rapidly using a smartphone. This paper proposes a 3D indoor positioning method based on the micro-electro-mechanical systems (MEMS) sensors of the smartphone. A quaternion-based robust adaptive cubature Kalman filter (RACKF) algorithm is proposed to estimate the heading of pedestrians based on magnetic, angular rate, and gravity (MARG) sensors. Then, the pedestrian behavior patterns are distinguished by detecting the changes of pitch angle, total accelerometer and barometer values of the smartphone in the duration of effective step frequency. According to the geometric information of the building stairs, the step length of pedestrians and the height difference of each step can be obtained when pedestrians go up and downstairs. Combined with the differential barometric altimetry method, the optimal height can be computed by the robust adaptive Kalman filter (RAKF) algorithm. Moreover, the heading and step length of each step are optimized by the Kalman filter to reduce positioning error. In addition, based on the indoor map vector information, this paper proposes a heading calculation strategy of the 16-wind rose map to improve the pedestrian positioning accuracy and reduce the accumulation error. Pedestrian plane coordinates can be solved based on the Pedestrian Dead-Reckoning (PDR). Finally, combining pedestrian plane coordinates and height, the three-dimensional positioning coordinates of indoor pedestrians are obtained. The proposed algorithm is verified by actual measurement examples. The experimental verification was carried out in a multi-story indoor environment. The results show that the Root Mean Squared Error (RMSE) of location errors is 1.04–1.65 m by using the proposed algorithm for three participants. Furthermore, the RMSE of height estimation errors is 0.17–0.27 m for three participants, which meets the demand of personal intelligent user terminal for location service. Moreover, the height parameter enables users to perceive the floor information.

Sensing gravity, the quantum way

Devices that exploit the extreme sensitivity of quantum states are making their way out of the lab and into everything from construction and healthcare to seismology. Michael Allen learns more about the technology that goes into building a quantum gravity sensor, and its multitude of uses in research and industry.

Shell-shaped condensates with gravitational sag: contact and dipolar interactions

We investigate Bose–Einstein condensates in bubble trap potentials in the presence of a small gravity. In particular, we focus on thin shells and study both contact and dipolar interacting condensates. We first analyze the effects of the anisotropic nature of the dipolar interactions, which already appear in the absence of gravity and are enhanced when the polarization axis of the dipoles and the gravity are slightly misaligned. Then, in the small gravity context, we investigate the dynamics of small oscillations of these thin, shell-shaped condensates triggered either by an instantaneous tilting of the gravity direction or by a sudden change of the gravity strength. This system could be a preliminary stage for realizing a gravity sensor in space laboratories.

Klasifikasi Physical Activity Berbasis Sensor Accelorometer, Gyroscope, dan Gravity menggunakan Algoritma Multi-class Ensemble GradientBoost

The current generation of smartphones is increasingly sophisticated, equipped with several sensors such as accelerometer, gravity sensor, and gyroscope that can be used to recognize human activities such as going up stairs, going down stairs, running and walking. To get information, the data will be grouped using statistical methods. The performance of statistical methods has shortcomings in classifying data because of the procedures that must be met. To cover this shortcoming, the ensemble technique is used. In this paper, we propose to apply the Multi-Class Ensemble Gradientboost algorithm to improve the performance of the logistic regression method in classifying such as climbing stairs, descending stairs, running and walking. The process of taking data using a smartphone by designing an Android-based .apk system. Then, the entire dataset was separated into training data and test data with a comparison percentage of 70:30. The results obtained show that the Multi-Class Ensemble Gradientboost algorithm succeeded in increasing the logistic regression performance by 27.93%

On the Air Buoyancy Effect in MEMS-Based Gravity Sensors for High Resolution Gravity Measurements

In this paper, the air buoyancy effect on Micro-Electro-MechanicalSystem (MEMS)-based gravity sensors for high-resolution gravity measurements is investigated. The MEMS gravimeter is operated in an atmospheric environment without any vacuum chamber; thus significantly simplifying the design, implementation and maintenance, and reducing the cost of the instrument. It is experimentally observed that the measured acceleration signal shows a clear correlation with the air buoyancy, and consequently the air pressure. A detailed theoretical model of the air buoyant force acting on the MEMS gravity sensor is proposed, giving a gravity-air pressure coefficient of 501.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula> Gal/hPa for the silicon springmass system. After removing the error introduced by the air buoyant force, the MEMS gravity sensor exhibits an ultra-low self-noise floor of 1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu {\mathrm {Gal}}/\sqrt{\rm Hz}$ </tex-math></inline-formula> @1 Hz, as well as an excellent stability, with an Allan deviation of 3 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu$ </tex-math></inline-formula> Gal (40 s integration time). The sensor is capable of measuring the Earth tides in a 16-day span. This discovery identified one major error source in high-resolution MEMS gravity sensors operating in atmosphere, which could potentially be useful for the development of future MEMS-based gravimeters.

Robust methods for estimating the orientation and position of IMU and MARG sensors

The inertial measurement unit (IMU) and magnetic, angular rate, and gravity (MARG) sensor orientation and position are widely used in the medical, robotics, and other fields. In general, the orientations can be defined by the integration of angular velocity data, and the positions are also computed from the double integration of acceleration data. However, the acceleration and angular velocity data are often inaccurate due to measurement errors which arise when the sensor moves quickly. Therefore, the orientations and positions significantly differ from the actual values. To address these issues, several techniques are proposed for the accurate measurement of IMU and MARG sensor orientations and positions. The proposed optimization method is applied to raw sensor data to compute faithful orientations by stabilizing and accelerating the convergence of the optimization process. Furthermore, a deep neural network based on 1D convolutional neural network (CNN) layers is proposed to predict the desired velocity from raw acceleration data. The method is validated qualitatively and quantitatively with an optical motion capture (mocap) system. The experimental results show that the proposed method significantly improves orientation and position estimations compared to those of other approaches.

Artificial intelligence-powered remote monitoring of patients with chronic obstructive pulmonary disease.

Artificial intelligence-powered remote monitoring of patients with chronic obstructive pulmonary disease.

Design of Intelligent Somatosensory Deformation Skateboard Based on STM32

Based on the research of the existing intelligent scooter, this paper puts forward a more perfect scheme for the skateboard with STM32 as the core from the aspects of appearance, internal performance and whether the skateboard is safe in the process of running. Firstly, the performance and serial port of STM32 are analyzed, and then the corresponding gravity sensor is selected according to the module and connected to the host chip. Then the simulation debugging of gravity sensing was carried out. After normal operation, the assembly of the whole skateboard was started and the Bluetooth module and LED module were installed. At the same time, a detachable handle is also designed in appearance to ensure driving safety to the greatest extent. Finally, the wireless communication control system is used to achieve humanized design, and the intelligent direction and speed are adjusted through the gravity induction module.

Lightweight Extended Kalman Filter for MARG Sensors Attitude Estimation

Low-power consumption of orientation estimation using low-cost inertial sensors are crucial for all the applications which are resource constrained critically. This paper presents a novel Lightweight quaternion-based Extended Kalman Filter (LEKF) for orientation estimation for magnetic, angular rate and gravity (MARG) sensors. In this filter, with employing the quaternion kinematic equation as the process model, we derived a simplified measurement model to create the lightweight system model for Kalman filtering, where the measurement model works efficiently, and the involved computation of measurement model is reduced. It’s later proved that the proposed filter saves time consumption. Further, observations of the accelerometer and the magnetometer are dealt with the proposed nonlinear measurement model, the good performance should be guaranteed in theory. For the experiments, a commercial sensor for data collection, and an optical system to provide reference measurements of orientation, namely Vicon, are utilized to investigate the performance of the proposed filter. Evaluation for different application scenarios are considered, which primarily includes human motion capture and the drone application. Results indicate that the proposed filter performs well for both applications, and is proved to have a good anti-vibration ability. What’s more, the comparison experiment shows that the proposed filter provides good performance, as same as the basic UKF, but it consumes the least computing time among the compared methods.

Bubble microcavity strain and gravity sensor with temperature and bending insensitivity using an ultra-thin core optical fiber

Recent progress in optical fiber sensors based on Fabry-Perot interferometers (FPIs) has achieved much attention. In this paper, we designed and fabricated an FPI sensor by building a bubble in an ultra-thin core optical fiber. The size of the bubble decreased gradually with further arc discharges, which was inverse to the bubble fabrication in standard single mode fibers and multimode fibers. The bubble microcavity in the ultra-thin core optical fiber demonstrated a strain sensitivity of 2.08 pm/με in the range of 0–5000 με and a gravity sensitivity of 2.908 nm/N in the range of 0–4.89 N with high linear responses. Meanwhile, the bubble microcavity sensor was insensitive to temperature and bending, which was benefit to avoid the cross influences. The FPI sensor based on a bubble microcavity in an ultra-thin core optical fiber possesses the properties of easy to fabricate, small size, robustness, and high sensitivity.

Nonlinear Parameter Estimation and Experimental Verification of Gravity Sensor Considering Quadratic Error Parameters

With the rapid development of inertial navigation technology, the proportion of inertial navigation technology in navigation and positioning is getting higher and higher. The measurement accuracy requirements of inertial components are getting higher and higher. Based on the mature linear calibration method, this article calibrates the quadratic error of the accelerometer component in the inertial component and compensates the error term. In theory, the least squares estimation is used to solve the quadratic parameter. After considering the quadratic term, the measurement accuracy of the gravity sensor has been improved, and the laboratory gravimeter has been used for marine experiments. The experimental results show that the positioning deviation considering the quadratic error term is smaller than the positioning deviation considering only the first error term.

Cascaded Indirect Kalman Filters for Land-Vehicle Attitude Estimation With MARG Sensors and GNSS Observations

This paper proposes a novel quaternion-based attitude estimation method for land-vehicle applications by fusing the low-cost magnetic, angular rate, and gravity (MARG) sensors and global navigation satellite systems measured velocity (GNSS-V). A structure of three-layer cascaded indirect Kalman filter (IKF) is devised to minimize the interactions of heterogeneous error sources on attitude quaternion solutions. It sequentially fuses the Earth's gravity vector, magnetic vector, and GNSS-V vector pairs with quaternion state constraints from Euler angles in each local filter. To further ensure the robustness of quaternion-based attitude solution, the external acceleration term is adaptively compensated within a sliding window of GNSS-V information. Meanwhile, independent and cross check procedures for observation vectors are also established for excluding individual sensor fault by taking land-vehicle dynamics into account. It is of significant importance that, in this contribution, the GNSS measured velocity is fully utilized in all potential aspects of aiding attitude determination with MARG sensors. Real-world vehicular experiments are carried out to evaluate the performance of our proposed method. The presented results verify the validity and efficiency thus the developed method may be promising for implemention in land-vehicle applications.

The Design of Smart Car Based on Android Client

This design is based on the open source Android version 8.0 system to develop the mobile phone platform client, using the eclipse integrated development environment to research and develop Bluetooth control. The remote control of the car is realized by Bluetooth technology, gravity sensor and voice. The design is mainly composed of hardware system and software system. It can realize the car’s forward, backward, rear left turn, rear right turn, front left turn, front right turn and other functions. This design scheme provides a new solution for the design and implementation of wireless remote control vehicle, and also plays a certain reference role for the design of remote control smart home system in the future.

Agreement and reliability of magnetic, angular rate, and gravity (MARG) sensors to assess multiple body segment’s external loads during off-road running

The extensive use of wearable sensors in sport medicine, exercise medicine, and health has increased the interest in their study. That is why it is necessary to test these technologies’ efficiency, effectiveness, agreement, and reliability in different settings. Consequently, the purpose of this article was to analyze the magnetic, angular rate, and gravity (MARG) sensor’s test-retest agreement and reliability when assessing multiple body segments’ external loads during off-road running. A total of 18 off-road runners (38.78 ± 10.38 years, 73.24 ± 12.6 kg, 172.17 ± 9.48 cm) ran two laps (1st and 2nd Lap) of a 12 km circuit wearing six MARG sensors. The sensors were attached to six different body segments: left (MPLeft) and right (MPRight) malleolus peroneus, left (VLLeft) and right (VLRight) vastus lateralis, lumbar (L1-L3), and thorax (T2-T4) using a special neoprene suit. After a principal component analysis (PCA) was performed, the total data set variance of all body segments was represented by 44.08%–70.64% for the 1st PCA factor considering two variables, Player LoadRT and Impacts, on L1-L3, respectively. These two variables were chosen among three total accelerometry-based external load indicators (ABELIs) to perform the agreement and reliability tests due to their relevance based on PCAs for each body segment. There were no significant differences between laps in the Player LoadRT or Impacts ( p &gt; 0.05, trivial). The intraclass correlation and lineal correlation showed a substantial to almost perfect over-time test consistency assessed via reliability in both Player LoadRT and Impacts. Bias and t-test assessments showed good agreement between Laps. It can be concluded that MARGs sensors offer significant test re-test reliability and good agreement when assessing off-road kinematics in the six different body segments.