Topic
Inertial measurement unit
About: Inertial measurement unit is a research topic. Over the lifetime, 13326 publications have been published within this topic receiving 189083 citations. The topic is also known as: IMU.
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11 Jun 2018
TL;DR: The main aim of the study is to compare the performances of 5 classifiers, based on machine learning, and show that the most suitable was DT, which can be easily implemented, it has low memory and computational requirements, and it allows for a further reduction of the required features.
Abstract: In recent years, there is a growing interest in Human Activity Recognition (HAR) systems applied in healthcare. A HAR system is essentially made of a wearable device equipped with a set of sensors (like accelerometers, gyroscopes, magnetometers, heart-rate sensors, etc…) and a classifier able to recognize the activity performed. In this study we focused on the choice of the classifier, since there isn’t a unique and consolidated methodology for HAR. The main aim of the study is to compare the performances of 5 classifiers, based on machine learning. Furthermore, we analyzed advantages and disadvantages of their implementation onto a wearable and realtime HAR system. We acquired magnetic and inertial measurement unit (MIMU) signals from 15 young volunteers. For each subject, we recorded 9 signals from tri-axis accelerometer, gyroscope and magnetometer. All signals were divided in 5s-windows and processed to extract 342 features in time, frequency and time-frequency domains. By means of two feature selection steps (correlation-based and genetic algorithm), we reduced the number of features to 69. These features were used as input for the following 5 classifiers: K-Nearest Neighbor (KNN), Feedforward Neural Network (FNN), Support Vector Machines (SVM), Naive Bayes (NB), and Decision Tree (DT). Our results showed that all classifiers were able to correctly recognize more than 90% of activities. The best performances were obtained by KNN. Analyzing advantages and disadvantages of each classifier for its implementation by means of a microcontroller the most suitable was DT. In fact, this classifier can be easily implemented, it has low memory and computational requirements, and it allows for a further reduction of the required features.
52 citations
TL;DR: This article considers the problem of measurement noise rejection in a linear output-feedback control system and proposes a novel noise estimator (NE)-based robust control solution, which takes into account not only the rejection of high-frequency stochastic noises but also the compensation for low-frequency measurement errors, such as bias and drift.
Abstract: This article considers the problem of measurement noise rejection in a linear output-feedback control system. Specifically, we take into account not only the rejection of high-frequency stochastic noises but also the compensation for low-frequency measurement errors, such as bias and drift, which cannot be well-handled by the classic frequency-domain filters or Kalman filters. A novel noise estimator (NE)-based robust control solution is proposed. The NE is designed in the frequency domain by exploiting the system model and control structure information and is embedded into the controller instead of being an independent functional module in the closed-loop system. The adverse effects of model uncertainties on the performance of the NE-based solution are investigated, and an improved solution is proposed by incorporating a simple low-pass filter as the prefilter of NE. This solution is applied to the angle tracking problem of a 2-DOF experimental helicopter platform equipped with a low-cost and low-accuracy microelectromechanical system (MEMS) inertial measurement unit (IMU) (MEMS IMU) for angular position/rate measurements. Both numerical simulation and experimental comparisons with other existing approaches demonstrate: 1) constant bias and time-varying drift in the IMU measurements are systematically addressed by the solution; 2) it is accessible to improve the steady-state tracking accuracy by tuning the parameter of NE to extend its bandwidth; and 3) when model uncertainties limit the feasible bandwidth of NE, the improved solution is able to largely maintain its noise rejection performance.
52 citations
TL;DR: Experimental results validate the theory and proved that the calibration and compensation method for mounting errors proposed in this paper helps improve the output attitude's precision without a precise installation.
Abstract: The rotational inertial navigation system (INS) has received wide attention in recent years because it can achieve high precision without using costly inertial sensors. However, the introduction of the turntable causes additional errors, including mounting errors between the inertial measurement unit (IMU) axes and the turntable axes. Analysis, calibration and compensation of the mounting errors are necessary in rotational INS. In this paper, the mounting errors are introduced into the sensor model of a dual-axis rotational INS. Analysing the improved model indicated that the mounting errors’ effect on the IMU errors is inconspicuous, but the effect on the output attitude is significant. If the output attitude is not required, the mounting errors can be ignored; conversely, it is important to calibrate and compensate for such errors. A calibration method for the mounting errors is designed using the thin-shell (TS) algorithm, and the method's precision has the same order of magnitude as the residuals of gyro misalignment in the simulation test. Laboratory experimental results validate the theory and proved that the calibration and compensation method for mounting errors proposed in this paper helps improve the output attitude's precision without a precise installation.
52 citations
TL;DR: A navigation system based on Elman Artificial Neural Network (ANN), which consists of MEMS sensors, which are based on IMU (Inertial Measurement Unit), which is a classic set of sensors for determining the position in space.
52 citations
23 Apr 2012
TL;DR: This study explains the methodology of how the deterministic errors are defined by 27 state static and 60 state dynamic rate table calibration test data and how they are used in the error compensation model.
Abstract: Inertial Measurement Units, the main component of a navigation system, are used in several systems today. IMU's main components, gyroscopes and accelerometers, can be produced at a lower cost and higher quantity. Together with the decrease in the production cost of sensors it is observed that the performances of these sensors are getting worse. In order to improve the performance of an IMU, the error compensation algorithms came into question and several algorithms have been designed. Inertial sensors contain two main types of errors which are deterministic errors like scale factor, bias, misalignment and stochastic errors such as bias instability and scale factor instability. Deterministic errors are the main part of error compensation algorithms. This study explains the methodology of how the deterministic errors are defined by 27 state static and 60 state dynamic rate table calibration test data and how those errors are used in the error compensation model. In addition, the stochastic error parameters, gyroscope and bias instability, are also modeled with Gauss Markov Model and instant sensor bias instability values are estimated by Kalman Filter algorithm. Therefore, accelerometer and gyroscope bias instability can be compensated in real time. In conclusion, this article explores how the IMU performance is improved by compensating the deterministic end stochastic errors. The simulation results are supported by real IMU test data.
51 citations