Data-Driven Denoising of Stationary Accelerometer Signals

Modern navigation solutions are largely dependent on the performances of the standalone inertial sensors, especially at times when no external sources are available. During these outages, the inertial navigation solution is likely to degrade over time due to instrumental noises sources, particularly when using consumer low-cost inertial sensors. Conventionally, model-based estimation algorithms are employed to reduce noise levels and enhance meaningful information, thus improving the navigation solution directly. However, guaranteeing their optimality often proves to be challenging as sensors performance differ in manufacturing quality, process noise modeling, and calibration precision. In the literature, most inertial denoising models are model-based when recently several data-driven approaches were suggested primarily for gyroscope measurements denoising. Data-driven approaches for accelerometer denoising task are more challenging due to the unknown gravity projection on the accelerometer axes. To fill this gap, we propose several learning-based approaches and compare their performances with prominent denoising algorithms, in terms of pure noise removal, followed by stationary coarse alignment procedure. Based on the benchmarking results, obtained in field experiments, we show that: (i) learning-based models perform better than traditional signal processing filtering; (ii) non-parametric kNN algorithm outperforms all state of the art deep learning models examined in this study; (iii) denoising can be fruitful for pure inertial signal reconstruction, but moreover for navigation-related tasks, as both errors are shown to be reduced up to one order of magnitude.Comment: 10 pages, 15 figures, 8 table

Characterization of errors and noises in MEMS inertial sensors using Allan variance method

This thesis work has addressed the problems of characterizing and identifying the noises inherent to inertial sensors as gyros and accelerometers, which are embedded in inertial navigation systems, with the purpose of estimating the errors on the obtained position. The analysis of the Allan Variance method (AVAR) to characterize and identify the noises related to these sensors, has been done. The practical implementation of the AVAR method for the noises characterization has been performed over an experimental setup using the IMU 3DM-GX3 -25 data and the Matlab environment. From the AVAR plots it was possible to identify the Angle Random Walk and the Bias Instability in the gyros, and the Velocity Random Walk and Bias Instability in the accelerometers. A denoising process was also performed by using the Discrete Wavelet Transforms and the Median Filter. After the filtering the AVAR plots showed that the ARW was almost removed or attenuated using Wavelets, but not good results were obtained with the Median Filter

Micro-Inertial-Aided High-Precision Positioning Method for Small-Diameter PIG Navigation

Pipeline leakage or explosion has caused huge economic losses, polluted the environments and threatened the safety of civilian’s lives and assets, which even caused negative influences to the society greatly. Fortunately, pipeline inspection gauge (PIG) could accomplish the pipeline defect (corrosions, cracks, grooves, etc.) inspection effectively and meanwhile to localize these defects precisely by navigation sensors. The results are utilized for pipeline integrity management (PIM) and pipeline geographic information system construction. Generally, the urban underground pipeline presents with small-diameter and complicated-distribution properties, which are of great challenges for the pipeline defects positioning by PIG. This chapter focuses on in-depth research of the high-precision positioning method for small-diameter PIG navigation. In the beginning, the problems and system errors statement of MEMS SINS-based PIG are analyzed step by step. Then, the pipeline junction (PJ) identification method based on fast orthogonal search (FOS) is studied. After that, a PIG positioning system that comprises of micro-inertial/AGM/odometer/PJ is proposed, and also the application mechanism of extended Kalman filter and its smoothing technology on PIG navigation system is researched to improve the overall positioning precision for the small-diameter PIG. Finally, the proposed methods and research conclusions are verified by the indoor wheel robot simulation platform

Neurological Tremor: Sensors, Signal Processing and Emerging Applications

Neurological tremor is the most common movement disorder, affecting more than 4% of elderly people. Tremor is a non linear and non stationary phenomenon, which is increasingly recognized. The issue of selection of sensors is central in the characterization of tremor. This paper reviews the state-of-the-art instrumentation and methods of signal processing for tremor occurring in humans. We describe the advantages and disadvantages of the most commonly used sensors, as well as the emerging wearable sensors being developed to assess tremor instantaneously. We discuss the current limitations and the future applications such as the integration of tremor sensors in BCIs (brain-computer interfaces) and the need for sensor fusion approaches for wearable solutions

Research on Signal Processing of MEMS Gyro Array

A new random drift model and the measured angular rate model of MEMS gyro are presented. Based on such models, signal processing techniques are used to decrease gyro drift. Kalman filtering equations have been built for static measurement and dynamic measurement of the gyro array, which combines N individual gyros into a single rate estimate. By selecting the favorable cross correlation coefficient between individual gyros in the noise correlation matrix, the gyro array performance can be significantly improved over that of any individual component device. A new gyro array dynamic measurement procession is also presented. Data fusion of the difference between individual gyro dynamic measurements can identify every gyro real-time drift out and get its noisy test. Based on the laws of the gyro curve motion, the tested dynamic signal is filtered to improve the gyro accuracy. All these processings have been implemented by digital signal processor. Simulation results show that the static drift can decrease from 22.1°/h to 0.184°/h and the dynamic drift can decrease from 22.1°/h to 8.98°/h

A modular approach for modeling, detecting, and tracking freezing of gait in Parkinson disease using inertial sensors

Parkinson disease, the second most common neurodegenerative disorder, is caused by the loss of dopaminergic subcortical neurons. Approximately 50% of people with Parkinson disease experience freezing of gait (FOG), a brief, episodic absence or marked reduction of forward progression of the feet despite the intention to walk. FOG causes falls and is resistant to medication in more than 50% of cases. FOG episodes can often be interrupted by mechanical interventions (e.g., a verbal reminder to march), but it is often not practical to apply these interventions on demand (e.g., there is not usually another person to detect an FOG episode and provide the reminder).Wearable sensors offer the possibility of detecting FOG episodes in real time and thus developing a “closed-loop” treatment: real-time detection can be coupled with on-demand interventions. Objective evaluation methods using wearable sensor technology to monitor and assess FOG have met with varying success. They do not use a signal model that captures FOG patterns explicitly, and they are of limited help in understanding the underlying mechanisms in the structure of the sensor data captured during FOG. In this dissertation, we first develop physically-based signal models for the sensor data, design statistical signal processing methods to detect FOG based on its patterns, and compute the probability of FOG. Then, we proceed to validate the system, using data from experimental gait assessment in a group of people with Parkinson disease.We further develop a modular approach to model, detect, and track FOG in Parkinson disease, using four modules, namely the detection, navigation, validation, and filtering modules. To capture the gait motion, we use an inertial measurement unit (IMU) consisting of a three-axis accelerometer and a three-axis gyroscope. We first build physically-based signal models that describe “no movement” and “trembling motion” during FOG events. In the detection module, we design a generalized likelihood ratio test framework to develop a two-stage detector for determining the zero-velocity event intervals (ZVEI) and trembling event intervals (TREI) that are associated with FOG. However, not all the detected TREI are associated with FOG. Therefore, to filter out the TREI which are not associated with FOG, we consider the fact that the alternating trembling motion in FOG is associated with low foot speeds and small pitch angles. Next, to estimate these gait parameters, we employ a zero-velocity aided inertial navigation system (ZV-INS) in the navigation module. The ZV-INS uses the ZVEI as pseudo measurements, along with a Kalman filter, to estimate the position, velocity, and orientation angles of the foot.To track the degradation of the gait parameters prior to the incidence of FOG, we detect valid gait cycles in the validation module. We first identify the non-stationary segments of the gyroscope signal in the sagittal plane, using ZVEI. Next, we preprocess the non-stationary segments by scaling and interpolating the signal. Finally, we validate the preprocessed non-stationary segment of the gyroscope signal in the sagittal plane as a valid gait cycle, using an optimization framework called sparsity-assisted wavelet denoising (SAWD). In the SAWD algorithm, we simultaneously combine low-pass filtering, multiresolution representations (wavelets), and a sparsity-inducing norm to obtain a sparse representation of the gyroscope signal in the sagittal plane for valid gait cycles, in the form of a discrete wavelet transform coefficient vector. We compute the root-mean-square error between the generated template and the sparse representation of the non-stationary segment of the gyroscope data in the sagittal plane, obtained using the SAWD algorithm. If the root-mean-square error is less than a fixed threshold, then the gait cycle is considered valid.Finally, to detect the onset and duration of FOG, we develop a point-process filter that computes the probability of FOG (pFOG). We model the edges of the TREI as a point-process, then assign weights to the edges, which depend on a participant-specific tunable parameter and the average value of the gait parameters observed in the bin containing the edge. To compute pFOG, we develop a Bayesian recursive filter and integrate the weights assigned to the edges of the TREI over a time window. To adaptively adjust the participant-specific tunable parameter, we develop two novel approaches that assign weights to the edges of the TREI based on the gait parameters extracted from the last valid gait cycle and the foot motion dynamics. We validate the performance of the modular system design using real data obtained from people with Parkinson disease who performed a battery of gait tasks known to trigger FOG. The results indicate improved performance, with an average accuracy greater than 85% and an average false positive rate of less than 14%. Altogether, we not only improve the accuracy of FOG detection but also open new avenues towards the development of low-cost remote health monitoring systems, which will help provide insights into the frequency and patterns of FOG that affect the quality of daily life in people with Parkinson disease

A comparison of machine learning classifiers for smartphone-based gait analysis

This paper proposes a reliable monitoring scheme that can assist medical specialists in watching over the patient's condition. Although several technologies are traditionally used to acquire motion data of patients, the high costs as well as the large spaces they require make them difficult to be applied in a home context for rehabilitation. A reliable patient monitoring technique, which can automatically record and classify patient movements, is mandatory for a telemedicine protocol. In this paper, a comparison of several state-of-the-art machine learning classifiers is proposed, where stride data are collected by using a smartphone. The main goal is to identify a robust methodology able to assure a suited classification of gait movements, in order to allow the monitoring of patients in time as well as to discriminate among a pathological and physiological gait. Additionally, the advantages of smartphones of being compact, cost-effective and relatively easy to operate make these devices particularly suited for home-based rehabilitation programs. Graphical Abstract. This paper proposes a reliable monitoring scheme that can assist medical specialists in watching over the patient's condition. Although several technologies are traditionally used to acquire motion data of patients, the high costs as well as the large spaces they require make them difficult to be applied in a home context for rehabilitation. A reliable patient monitoring technique, which can automatically record and classify patient movements, is mandatory for a telemedicine protocol. In this paper, a comparison of several state-of-the-art machine learning classifiers is proposed, where stride data are collected and processed by using a smartphone(see figure). The main goal is to identify a robust methodology able to assure a suited classification of gait movements, in order to allow the monitoring of patients in time as well as to discriminate among a pathological and physiological gait. Additionally, the advantages of smartphones of being compact, cost-effective and relatively easy to operate make these devices particularly suited for home-based rehabilitation programs

Using BP Neural Network and Kalman Filter to Signal Processing of MEMS Inertial Sensors

The paper put forward using BP neural network and Kalman filter to signal processing of MEMS inertial sensors. This paper uses Kalman filter value for information fusion of gyroscope and accelerometer, and the attitude angle is accurate. The state and observation equation of attitude angle measuring system with characteristic of BP neural network, and the design of the Kalman filter is simple and gyroscope measurement information data fusion, the preparation of the corresponding MATLAB program Kalman filter is designed. Through the simulation results of the image, this method can compensate the zero drift of gyroscope, improves the measurement precision of the attitude angle

A Novel Dynamic Measurement System for Evaluating the Braking Coordination of Articulated Vehicles

The braking coordination between tractor and semitrailer is vital to the safety of articulated vehicles. Traditional evaluation about braking coordination is based on the pressure measurement along air braking pipeline, which needs to change original braking structure to install gauges and cannot directly reflect the final braking coordination of different wheels. To overcome these limitations, this paper proposes a novel dynamic measurement system for evaluating the braking coordination of articulated vehicles. During the brake test, all wheel velocities of the whole articulated vehicle are synchronously obtained through a specially designed distributed acquisition platform. To effectively eliminate gross errors and noises in wheel angular velocity data, a 3-order autoregressive (AR) model and an improved-thresholding wavelet filtering algorithm are developed. Further, a novel direct evaluation method about braking coordination is proposed according to the differences in angular velocity dropping time of all wheels. Finally, the overall system is assessed through real field tests. The results validate the feasibility and effectiveness of the proposed system