Miniature Inertial Measurement Unit Research Articles

Soft-thresholding Alternating Minimization Optimization Algorithm for Turntable-free Calibration Based on the Miniature Inertial Measurement Unit

Soft-thresholding Alternating Minimization Optimization Algorithm for Turntable-free Calibration Based on the Miniature Inertial Measurement Unit

Acquisition and cognition information of human body swing

Man plays an important role as the moving sensor platform in information systems, including 6G communication and intelligent applications. In particular, personal self-position and attitude are critical for firefighters performing rescues, athletes competing in sports, and the shooter with a shoulder launcher aiming at a target. Due to the swinging of the human body and respiratory fluctuations, the real-time miniature inertial measurement unit (MIMU) data combines body motion noise and degrades navigation precision. Two scenarios are discussed in this paper, including aiming at a stationary and moving target. The angular rate and acceleration of MIMU data for a stationary target are obtained from the turntable and shooter's shoulder, respectively. Data are collected via a stimulated launcher mounted on the shooter's shoulder and the theoretical solution for a moving target. A technology known as error cumulative amplitude spectrum (CAS) is adapted to extract the specific frequency caused by the shooter's body swing, which exhibits low-frequency characteristics. Thus, a mixed filter frame composed of an adaptive notch filter (ANF) and a cubature Kalman filter (CKF) is described in detail to reduce both high- and low-frequency measurement errors. The results of series tests indicate that the acceleration and angular rate errors caused by shooter swing are reduced by more than 98% and more than 81%, respectively, in the stationary and moving cases. As a result, the presented method effectively suppresses body swing and enhances navigation performance.

A Robust Quaternion Kalman Filter Method for MIMU/GPS In-Motion Alignment

In this article, a novel robust initial alignment method for the strapdown inertial navigation system (SINS) and global positioning system (GPS) integrated system is proposed to eliminate the outliers containing in the outputs of GPS. There are two innovations in the proposed method. First, the traditional quaternion Kalman filter method is often used to estimate the constant attitude quaternion in the swaying base. However, this article devises the new quaternion state model, which can be used in the in-motion base, expanding the scope of application. Second, based on the Huber's robust theory and the in-motion quaternion Kalman filter state model, the novel algorithm called the robust quaternion Kalman filter method is devised. This method can construct the improved observation vectors to eliminate the outliers and estimate the constant attitude quaternion more accurately than the traditional in-motion method when GPS contains outliers. The experiment using the miniature inertial measurement unit (MIMU)-based SINS aided by GPS has shown the effectiveness of the proposed method.

Deep heading estimation for pedestrian dead reckoning

Currently, the heading estimation could be easily achieved by many built-in direction sensors, such as the smartphone. However, the obtained heading angle is only suitable for situations where an equipped pedestrian’s movement and orientation keep the same, such as normal forward walking and turning. When the pedestrian faces a different direction from his movement, the heading angle remains the pedestrian orientation but not the movement and thus causes a heading estimation error. In this paper, we introduce several related deep learning techniques to explore their respective abilities in heading estimation of the waist-mounted Miniature Inertial Measurement Unit (MIMU). Specifically, this paper adopts two kinds of methods to analyze the data collected from MIMU, include acceleration, angular velocity, or their combination, to predict the heading angle. Firstly, considering the heading estimation is a time series prediction problem actually, this paper introduces the powerful Long Short-Time Memory (LSTM) model. On the other hand, this paper uses the Graph Convolutional Network (GCN) model to consider the relationship between the orientation and the direction of motion at different times. In experiments, we show that the accuracy of the proposed LSTM model in the test set achieved a promising 99.12%. To test our method in the real scenes, this paper designs simulation experiments and mobile terminal tests based on Tensorflow Lite. The experimental results show that the movement heading can be effectively judged based on the waist-mounted sensor data, and has a very significant accuracy.

Comment on "Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensor".

The recent paper “Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensor” (Sensors 2020, 20, 354) proposes a wearable system based on a foot-worn miniature inertial measurement unit (MIMU) and different methods to detect hurdle clearance and to identify the leading leg during 400-m hurdle races. Furthermore, the presented system identifies changes in contact time, flight time, running speed, and step frequency throughout the race. In this comment, we discuss the original paper with a focus on the ecological validity and the applicability of MIMU systems for field-based settings, such as training or competition for elite athletes.

Magnetic-Based Positioning System for Moving Target With Feature Vector

Underground, indoor, and harsh environments, which are complex, mutable, and hard to model, prevent high accuracy positioning and navigation. In this paper, a novel positioning system suitable to such environments is proposed. This system is based on a magnetic feature vector integrated with a miniature inertial measurement unit (MIMU). Compared with traditional magnetic-based positioning systems (MBPS), the proposed system exhibits better robustness, and applicability, which is proved by experimental results. Its non-accumulated error characteristics and outstanding penetrability enable the system to provide long-time accurate position and attitude service in underground, indoor, and harsh environments. The experiments indicate that the expected precision of the system can reach 0.055 and 0.062 m and an angle error of 2.1° and 2.8° in line of sight (LOS) and non-line of sight (NLOS) environments, respectively, in the static experiment and 0.051 m in the moving target tracking experiment.

Measurement of human rotation behavior for psychological and neuropsychological investigations.

The investigation of rotation behavior in human beings enjoys a longstanding and enduring interest in laterality research. While in animal studies the issue of accurately measuring the number of rotations has been solved and is widely applied in practice, it is still challenging to assess the rotation behavior of humans in daily life. We propose a robust method to assess human rotation behavior based on recordings from a miniature inertial measurement unit that can be worn unobtrusively on a belt. We investigate the effect of different combinations of low-cost sensors-including accelerometers, gyroscopes, and magnetometers-on rotation measurement accuracy, propose a simple calibration procedure, and validate the method on data from a predefined path through and around buildings. Results suggest that a rotation estimation based on the fusion of accelerometer, gyroscope, and magnetometer measurements outperforms methods based solely on earth magnetic field measurements, as proposed in previous studies, by a drop in error rate of up to 32 %. We further show that magnetometer signals do not significantly contribute to measurement accuracy in short-term measurements, and could thus be omitted for improved robustness in environments with magnetic field disturbances. Results also suggest that our simple calibration procedure can compete with more complex approaches and reduce the error rate of the proposed algorithm by up to 38 %.

Sports Signal Acquisition Based on Miniature Inertial Measurement Unit

The proposed method for Miniature Inertial Measurement Unit (MIMU) system enables the exact measurement of sports parameter even under challenging conditions. With the aid of sports biomechanical parameter, proposed method suppresses the drift. The Hilbert-spectrum responds the sports rhythm law. It will provide aided information for trainer.

Reconstructing Free-Flight Angular Velocity from a Miniaturized Wireless Accelerometer

The theory governing the torque-free motion of a rigid body is well established, yet direct experimental measurement in the laboratory remains an obvious challenge. This paper addresses this challenge by presenting a novel miniature wireless inertial measurement unit (IMU) that directly measures the motion of a rigid body during free-flight. The IMU incorporates three-axis sensing of acceleration and three-axis sensing of angular velocity with a microcontroller and an RF transceiver for wireless data transmission to a host computer. Experiments consider a rigid body that is spun up by hand and then released into free-flight. The measured rotational dynamics from the IMU are carefully benchmarked against theoretical predictions. This benchmarking reveals that the angular velocity directly measured by the angular rate gyros lies within 6% of that predicted by the (Jacobi elliptic function) solutions to the Euler equations. Moreover, experimentally constructed polhodes elegantly illustrate the expected stable precession for rotations initiated close to the major or minor principal axes and the unstable precession for rotations initiated close to the intermediate axis. We then present a “gyro-free” design that employs a single, triaxial accelerometer to reconstruct the angular velocity during free-flight. A measurement theory is presented and validated experimentally. Results confirm that the angular velocity can be reconstructed with exceedingly small errors (less than 2%) when benchmarked against direct measurements using angular rate gyros. The simpler gyro-free design addresses restrictions imposed by rate gyro cost, size, and measurement range and may enable high-volume commercial applications of this technology in instrumented baseballs, basketballs, golf balls, footballs, soccer balls, softballs, and the like.

A doubly decoupled lateral axis micromachined gyroscope

In this paper, a doubly decoupled x-axis gyroscope with novel torsional sensing comb capacitors is presented. The doubly decoupled design is more efficient to suppress mechanical coupling of gyroscope. Both driving and sensing modes of the gyroscope are dominated by slide film air damping, therefore, it can work even at atmospheric pressure. Moreover, the sensing capacitors adopt asymmetrical comb fingers so that they can differentially detect out-of-plane torsional movements. The fabrication process of the gyroscope is compatible with z-axis gyroscope process, which makes it potential to realize low cost monolithic MIMU (miniature inertial measurement unit) without vacuum packaging. The gyroscope was fabricated and tested. The sensitivity is 22 mV/°/s while the nonlinearity is 2.19% at atmosphere. The noise floor is 0.02°/s/Hz 1/2.