A new method to reduce the noise of the miniaturised inertial sensors disposed in redundant linear configurations

2013 ◽  
Vol 117 (1188) ◽  
pp. 111-132 ◽  
Author(s):  
T. L. Grigorie ◽  
R. M. Botez
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Frequency ◽  
Minimum Variance ◽  
Measurement Unit ◽  
Time Step ◽  
Accelerometer Sensor ◽  
Sensor Model ◽  
Input Acceleration

Abstract This paper presents a new adaptive algorithm for the statistical filtering of miniaturised inertial sensor noise. The algorithm uses the minimum variance method to perform a best estimate calculation of the accelerations or angular speeds on each of the three axes of an Inertial Measurement Unit (IMU) by using the information from some accelerometers and gyros arrays placed along the IMU axes. Also, the proposed algorithm allows the reduction of both components of the sensors’ noise (long term and short term) by using redundant linear configurations for the sensors dispositions. A numerical simulation is performed to illustrate how the algorithm works, using an accelerometer sensor model and a four-sensor array (unbiased and with different noise densities). Three cases of ideal input acceleration are considered: 1) a null signal; 2) a step signal with a no-null time step; and 3) a low frequency sinusoidal signal. To experimentally validate the proposed algorithm, some bench tests are performed. In this way, two sensors configurations are used: 1) one accelerometers array with four miniaturised sensors (n = 4); and 2) one accelerometers array with nine miniaturised sensors (n = 9). Each of the two configurations are tested for three cases of input accelerations: 0ms−1, 9·80655m/s2 and 9·80655m/s2.

scholarly journals A Container-Attachable Inertial Sensor for Real-Time Hydration Tracking

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4008 ◽  
Author(s):  
Henry Griffith ◽  
Yan Shi ◽  
Subir Biswas
Keyword(s):  
Regression Models ◽  
Large Scale ◽  
Inertial Sensors ◽  
Mutual Influence ◽  
Inertial Sensor ◽  
Measurement Unit ◽  
Percentage Error ◽  
Support Vector ◽  
Accelerometer Sensor ◽  
Fill Level

Various sensors have been proposed to address the negative health ramifications of inadequate fluid consumption. Amongst these solutions, motion-based sensors estimate fluid intake using the characteristics of drinking kinematics. This sensing approach is complicated due to the mutual influence of both the drink volume and the current fill level on the resulting motion pattern, along with differences in biomechanics across individuals. While motion-based strategies are a promising approach due to the proliferation of inertial sensors, previous studies have been characterized by limited accuracy and substantial variability in performance across subjects. This research seeks to address these limitations for a container-attachable triaxial accelerometer sensor. Drink volume is computed using support vector machine regression models with hand-engineered features describing the container’s estimated inclination. Results are presented for a large-scale data collection consisting of 1908 drinks consumed from a refillable bottle by 84 individuals. Per-drink mean absolute percentage error is reduced by 11.05% versus previous state-of-the-art results for a single wrist-wearable inertial measurement unit (IMU) sensor assessed using a similar experimental protocol. Estimates of aggregate consumption are also improved versus previously reported results for an attachable sensor architecture. An alternative tracking approach using the fill level from which a drink is consumed is also explored herein. Fill level regression models are shown to exhibit improved accuracy and reduced inter-subject variability versus volume estimators. A technique for segmenting the entire drink motion sequence into transport and sip phases is also assessed, along with a multi-target framework for addressing the known interdependence of volume and fill level on the resulting drink motion signature.

Wireless Input Technology Based on MEMS Inertial Measurement Unit - A Survey

2017 ◽  
Vol 870 ◽  
pp. 79-84
Author(s):  
Zhen Xian Fu ◽  
Guang Ying Zhang ◽  
Yu Rong Lin ◽  
Yang Liu
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Propagation Model ◽  
Measurement Unit ◽  
Input Device ◽  
Operational Environment ◽  
Inertial Measurement ◽  
Mems Imu

Rapid progress in Micro-Electromechanical System (MEMS) technique is making inertial sensors increasingly miniaturized, enabling it to be widely applied in people’s everyday life. Recent years, research and development of wireless input device based on MEMS inertial measurement unit (IMU) is receiving more and more attention. In this paper, a survey is made of the recent research on inertial pens based on MEMS-IMU. First, the advantage of IMU-based input is discussed, with comparison with other types of input systems. Then, based on the operation of an inertial pen, which can be roughly divided into four stages: motion sensing, error containment, feature extraction and recognition, various approaches employed to address the challenges facing each stage are introduced. Finally, while discussing the future prospect of the IMU-based input systems, it is suggested that the methods of autonomous and portable calibration of inertial sensor errors be further explored. The low-cost feature of an inertial pen makes it desirable that its calibration be carried out independently, rapidly, and portably. Meanwhile, some unique features of the operational environment of an inertial pen make it possible to simplify its error propagation model and expedite its calibration, making the technique more practically viable.

Requirements for inertial sensor systems for measuring robot positions

Robotica ◽  
1990 ◽  
Vol 8 (2) ◽  
pp. 145-150 ◽  
Author(s):  
H. Janocha ◽  
D. Schmidt
Keyword(s):  
Autonomous Navigation ◽  
Moving Objects ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Sensor Technology ◽  
Inertial Measurement ◽  
Measurement Systems ◽  
Land Water ◽  
Intense Research

SummaryInertial Measurement Systems (IMS) allow the position calculation of moving objects without requiring outside information. For years the inertial 3-D coordinate measuring technique has been subject to intense research in geodesy and autonomous navigation of land-, water-and airborne vehicles. Because of these areas of application inertially-based systems have been designed for long term measuring only. Here we discuss the requirements that are imposed on inertial sensors in order for them to be used for the calculation of positions of robots. The use of modern sensor technology, combined with strategies for error correction, can result in substantial advantages when calculating robot positions independently from load and environment.

scholarly journals Validation of a Low-Cost Pavement Monitoring Inertial-Based System for Urban Road Networks

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3127
Author(s):  
Giuseppe Loprencipe ◽  
Flavio Guilherme Vaz de Almeida Filho ◽  
Rafael Henrique de Oliveira ◽  
Salvatore Bruno
Keyword(s):  
High Performance ◽  
Ride Comfort ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Road Networks ◽  
Measurement Unit ◽  
Raspberry Pi ◽  
Urban Road ◽  
Comfort Index

Road networks are monitored to evaluate their decay level and the performances regarding ride comfort, vehicle rolling noise, fuel consumption, etc. In this study, a novel inertial sensor-based system is proposed using a low-cost inertial measurement unit (IMU) and a global positioning system (GPS) module, which are connected to a Raspberry Pi Zero W board and embedded inside a vehicle to indirectly monitor the road condition. To assess the level of pavement decay, the comfort index awz defined by the ISO 2631 standard was used. Considering 21 km of roads with different levels of pavement decay, validation measurements were performed using the novel sensor, a high performance inertial based navigation sensor, and a road surface profiler. Therefore, comparisons between awz determined with accelerations measured on the two different inertial sensors are made; in addition, also correlations between awz, and typical pavement indicators such as international roughness index, and ride number were also performed. The results showed very good correlations between the awz values calculated with the two inertial devices (R2 = 0.98). In addition, the correlations between awz values and the typical pavement indices showed promising results (R2 = 0.83–0.90). The proposed sensor may be assumed as a reliable and easy-to-install method to assess the pavement conditions in urban road networks, since the use of traditional systems is difficult and/or expensive.

Quantifying Throw Counts and Intensities Throughout a Season in Youth Baseball Players: A Pilot Study

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Michael J. Rose ◽  
Katherine A. McCollum ◽  
Michael T. Freehill ◽  
Stephen M. Cain
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Study Data ◽  
Measurement Unit ◽  
Support Vector ◽  
Identification Algorithm ◽  
Machine Model ◽  
Baseball Players ◽  
Youth Baseball ◽  
Baseball Team

Abstract Overuse injuries in youth baseball players due to throwing are at an all-time high. Traditional methods of tracking player throwing load only count in-game pitches and therefore leave many throws unaccounted for. Miniature wearable inertial sensors can be used to capture motion data outside of the lab in a field setting. The objective of this study was to develop a protocol and algorithms to detect throws and classify throw intensity in youth baseball athletes using a single, upper arm-mounted inertial sensor. Eleven participants from a youth baseball team were recruited to participate in the study. Each participant was given an inertial measurement unit (IMU) and was instructed to wear the sensor during any baseball activity for the duration of a summer season of baseball. A throw identification algorithm was developed using data from a controlled data collection trial. In this report, we present the throw identification algorithm used to identify over 17,000 throws during the 2-month duration of the study. Data from a second controlled experiment were used to build a support vector machine model to classify throw intensity. Using this classification algorithm, throws from all participants were classified as being “low,” “medium,” or “high” intensity. The results demonstrate that there is value in using sensors to count every throw an athlete makes when assessing throwing load, not just in-game pitches.

A Comprehensive Overview of Inertial Sensor Calibration Techniques

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Shashi Poddar ◽  
Vipan Kumar ◽  
Amod Kumar
Keyword(s):  
High Precision ◽  
Kalman Filtering ◽  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Measurement Unit ◽  
Sensor Calibration ◽  
Comprehensive Overview ◽  
Inertial Measurement ◽  
Calibration Techniques

Inertial measurement unit (IMU) comprising of the accelerometer and gyroscope is prone to various deterministic errors like bias, scale factor, and nonorthogonality, which need to be calibrated carefully. In this paper, a survey has been carried out over different calibration techniques that try to estimate these error parameters. These calibration schemes are discussed under two broad categories, that is, calibration with high-end equipment and without any equipment. Traditional calibration techniques use high-precision equipment to generate references for calibrating inertial sensors and are generally laboratory-based setup. Inertial sensor calibration without the use of any costly equipment is further studied under two subcategories: ones based on multiposition method and others with Kalman filtering framework. Later, a brief review of vision-based inertial sensor calibration schemes is also provided in this work followed by a discussion which indicates different shortcomings and future scopes in the area of inertial sensor calibration.

Implementation and Analysis of Tightly Integrated INS/Stereo VO for Land Vehicle Navigation

2017 ◽  
Vol 71 (1) ◽  
pp. 83-99 ◽  
Author(s):  
Fei Liu ◽  
Yashar Balazadegan Sarvrood ◽  
Yang Gao
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Local Level ◽  
Visual Odometry ◽  
Satellite System ◽  
Velocity Estimation ◽  
Measurement Unit ◽  
Land Vehicle ◽  
Tight Integration ◽  
Stereo Visual Odometry

Tight integration of inertial sensors and stereo visual odometry to bridge Global Navigation Satellite System (GNSS) signal outages in challenging environments has drawn increasing attention. However, the details of how feature pixel coordinates from visual odometry can be directly used to limit the quick drift of inertial sensors in a tight integration implementation have rarely been provided in previous works. For instance, a key challenge in tight integration of inertial and stereo visual datasets is how to correct inertial sensor errors using the pixel measurements from visual odometry, however this has not been clearly demonstrated in existing literature. As a result, this would also affect the proper implementation of the integration algorithms and their performance assessment. This work develops and implements the tight integration of an Inertial Measurement Unit (IMU) and stereo cameras in a local-level frame. The results of the integrated solutions are also provided and analysed. Land vehicle testing results show that not only the position accuracy is improved, but also better azimuth and velocity estimation can be achieved, when compared to stand-alone INS or stereo visual odometry solutions.

scholarly journals Fusing Stereo Camera and Low-Cost Inertial Measurement Unit for Autonomous Navigation in a Tightly-Coupled Approach

2014 ◽  
Vol 68 (3) ◽  
pp. 434-452 ◽  
Author(s):  
Zhiwen Xian ◽  
Xiaoping Hu ◽  
Junxiang Lian
Keyword(s):  
Autonomous Navigation ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Three Dimensional ◽  
Fast Response ◽  
Location Estimation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Stereo Camera

Exact motion estimation is a major task in autonomous navigation. The integration of Inertial Navigation Systems (INS) and the Global Positioning System (GPS) can provide accurate location estimation, but cannot be used in a GPS denied environment. In this paper, we present a tight approach to integrate a stereo camera and low-cost inertial sensor. This approach takes advantage of the inertial sensor's fast response and visual sensor's slow drift. In contrast to previous approaches, features both near and far from the camera are simultaneously taken into consideration in the visual-inertial approach. The near features are parameterised in three dimensional (3D) Cartesian points which provide range and heading information, whereas the far features are initialised in Inverse Depth (ID) points which provide bearing information. In addition, the inertial sensor biases and a stationary alignment are taken into account. The algorithm employs an Iterative Extended Kalman Filter (IEKF) to estimate the motion of the system, the biases of the inertial sensors and the tracked features over time. An outdoor experiment is presented to validate the proposed algorithm and its accuracy.

scholarly journals Mobile Robot Indoor Positioning Based on a Combination of Visual and Inertial Sensors

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1773 ◽  
Author(s):  
Mingjing Gao ◽  
Min Yu ◽  
Hang Guo ◽  
Yuan Xu
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Indoor Positioning ◽  
Indoor Navigation ◽  
Depth Image ◽  
Measurement Unit ◽  
Translation Vector ◽  
Visual Sensor

Multi-sensor integrated navigation technology has been applied to the indoor navigation and positioning of robots. For the problems of a low navigation accuracy and error accumulation, for mobile robots with a single sensor, an indoor mobile robot positioning method based on a visual and inertial sensor combination is presented in this paper. First, the visual sensor (Kinect) is used to obtain the color image and the depth image, and feature matching is performed by the improved scale-invariant feature transform (SIFT) algorithm. Then, the absolute orientation algorithm is used to calculate the rotation matrix and translation vector of a robot in two consecutive frames of images. An inertial measurement unit (IMU) has the advantages of high frequency updating and rapid, accurate positioning, and can compensate for the Kinect speed and lack of precision. Three-dimensional data, such as acceleration, angular velocity, magnetic field strength, and temperature data, can be obtained in real-time with an IMU. The data obtained by the visual sensor is loosely combined with that obtained by the IMU, that is, the differences in the positions and attitudes of the two sensor outputs are optimally combined by the adaptive fade-out extended Kalman filter to estimate the errors. Finally, several experiments show that this method can significantly improve the accuracy of the indoor positioning of the mobile robots based on the visual and inertial sensors.

scholarly journals Estimation and Error Analysis for Optomechanical Inertial Sensors

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6101
Author(s):  
Patrick Kelly ◽  
Manoranjan Majji ◽  
Felipe Guzmán
Keyword(s):  
Kalman Filter ◽  
Inertial Sensors ◽  
State Of The Art ◽  
Inertial Sensor ◽  
Measurement Noise ◽  
Sensor Model ◽  
Sensing Technology ◽  
Operation Period ◽  
Extended Operation ◽  
Calibration Step

A sensor model and methodology to estimate the forcing accelerations measured using a novel optomechanical inertial sensor with the inclusion of stochastic bias and measurement noise processes is presented. A Kalman filter for the estimation of instantaneous sensor bias is developed; the outputs from this calibration step are then employed in two different approaches for the estimation of external accelerations applied to the sensor. The performance of the system is demonstrated using simulated measurements and representative values corresponding to a bench-tested 3.76 Hz oscillator. It is shown that the developed methods produce accurate estimates of the bias over a short calibration step. This information enables precise estimates of acceleration over an extended operation period. These results establish the feasibility of reliably precise acceleration estimates using the presented methods in conjunction with state of the art optomechanical sensing technology.

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