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Adaptive Gain Regulation of sensor fusion algorithms for Orientation Estimation with Magnetic and Inertial Measurement Units

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2021年 70卷 1-13页

作者： Nazarahari, Milad Rouhani, Hossein Univ Alberta Dept Mech Engn Neuromuscular Control & Biomech Lab Edmonton AB T6G 2R3 Canada

Magnetic and inertial measurement units (MIMUs) proved to he an accurate alternative for optic, electromagnetic, or acoustic measurement technologies. While the orientation of the MIMU could he estimated using accelerometer, gyroscope, or magnetometer sensors alone, many studies proposed sensor fusion algorithms (SFAs) to overcome the drawbacks that appear when each sensor is used individually. However, the performance of such SFAs highly depends on their gains, and poor initialization or incorrect adjustment of the gains would degrade the SFAs' performance. Therefore, this article proposes a general framework to find the optimal adaptive gain tuning scheme for Kalman filters and complementary filters to achieve accurate and robust orientation estimation with MIMUs. To this end, we proposed an innovative optimization framework to find the fixed optimal gain of an SFA or the optimal adaptive gain regulation scheme. Also, we demonstrated that the designed adaptive gain regulation scheme (a hard switch with two or three levels or a fuzzy inference system) is essential for orientation tracking with various SFAs. We measured the thigh, shank, and foot motion of nine participants while performing various activities using MIMUs and a camera motion-capture system to calculate the MIMUs' error in 3-D angle estimations. Gain regulation by hard switch was significantly (p < 0.05) more accurate and robust than it was for innovation adaptive estimation. Also, for all tested SFAs, hard switching for shank and foot MIMUs was significantly more accurate or robust than that for fixed optimal gain. Our experimental results showed that the adaptive gain tuning of SFAs using optimized gains is crucial, regardless of the algorithm structure or complexity.

关键词： Human motion analysis inertial measurement units inertial navigation systems sensor fusion algorithms Kalman filters complementary filters

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A survey of sensor fusion algorithms for sport and health monitoring applications 42

A survey of sensor fusion algorithms for sport and health mo...

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42nd Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Pedro Amaro, J. Patrao, Sergio Polytech Inst Coimbra Dept Elect Engn Coimbra Portugal

ISBN: (纸本)9781509034741

The development of Micro-Electro-Mechanical Systems based inertial sensors allows the implementation of a large number of commercial and scientific solutions for, health and sports monitoring, navigation systems and aeronautical/aerospace engineering applications. Inertial sensors widespread the so called embedded systems also prompted for the development of algorithms to cope with their inherent inaccuracy. These sensors, with small form factor and low cost, present an advantage for computing three-dimensional space object orientation. Also radio frequency communication devices are frequently associated with inertial sensor and embedded systems. Radio frequency communication protocols allow immediate data transfer and processing without cumbersome cabling. This work presents a survey of a number of relevant sensor fusion algorithms. The identified algorithms are implemented and its performance tested. Also a Yaskawa Motoman robotic arm is used to implement a set of predefined movements that granted the mandatory test accuracy and repeatability conditions. A Bluetooth Low Energy device has been implemented to transfer the inertial sensor data to a PC computer running a Windows operating system. The inertial sensors have been implemented with a MPU-9150 Inertial Measurement Unit (IMU) that includes an accelerometer, a gyroscope and a compass.

关键词： Bluetooth Low Energy movement monitoring robotic arm sensor fusion algorithms

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Influence of Sampling Rate on Wearable IMU Orientation Estimation Accuracy for Human Movement Analysis

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sensorS 2025年第7期25卷 1976-1976页

作者： Fan, Bingfei Zhang, Luobin Cai, Shibo Du, Mingyu Liu, Tao Li, Qingguo Shull, Peter Zhejiang Univ Technol Coll Mech Engn Hangzhou 310014 Peoples R China Zhejiang Univ Technol Coll Mech Engn Key Lab Special Purpose Equipment & Adv Proc Techn Minist Educ & Zhejiang Prov Hangzhou 310014 Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Queens Univ Dept Mech & Mat Engn Kingston ON K7L 3N6 Canada Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China

Wearable inertial measurement units (IMUs) have been widely used in human movement analysis outside the laboratory. However, the IMU-based orientation estimation remains challenging, particularly in scenarios involving relatively fast movements. Increased sampling rate has the potential to improve accuracy, but it also increases power consumption and computational complexity. The relationship between sampling frequencies and accuracies remains unclear. We thus investigated the specific influence of IMU sampling frequency on orientation estimation across a spectrum of movement speeds and recommended sufficient sampling rates. Seventeen healthy subjects wore IMUs on their thigh, shank, and foot and performed walking (1.2 m/s) and running (2.2 m/s) trials on a treadmill, and a motion testbed with an IMU was used to mimic high-frequency cyclic human movements up to 3.0 Hz. Four widely used IMU sensor fusion algorithms computed orientations at 10, 25, 50, 100, 200, 400, 800, and 1600 Hz and were compared with marker-based optical motion capture (OMC) orientations to determine accuracy. Results suggest that the sufficient IMU sampling rate for walking is 100 Hz, running is 200 Hz, and high-speed cyclic movements is 400 Hz. The accelerometer sampling rate is less important than the gyroscope sampling rate. Further, accelerometer sampling rates exceeding 100 Hz even resulted in decreased accuracy because excessive orientation updates using distorted accelerations and angular velocity introduced more error than merely using angular velocity. These findings could serve as a foundation to inform wearable IMU development or selection across a spectrum of human gait movement speeds.

关键词： inertial measurement unit strap-down integration high-rate gyroscope orientation estimation sensor fusion algorithms sampling rate influence

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sensor fusion for simple walking robot using low-level implementation of Extended Kalman Filter

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IFAC-PapersOnLine 2018年第13期51卷 43-48页

作者： Anderle, Milan elikovský, Sergej The Czech Academy of Sciences Institute of Information Theory and Automation Czech Republic

The main aim of this paper depicts in design and implementation of the Extended Kalman Filter for a nonlinear system in an application of a sensor fusion from a practical point of view. The sensor fusion is a typical data processing problem in mechanical systems where individual measurements of (angular) positions, velocities or accelerations are done independently on each other but the measured values are correlated to each other via dynamics of the system. Moreover, the measurement is corrupted by noise. The sensor fusion technique is capable to gain proper information about positions, velocities or accelerations from inaccurate measurement. In background of the sensor fusion algorithm, in our particular case, works the Extended Kalman Filter. Its adaptation for a simple mechanical system represented by a nonlinear system are object of the research in this paper related to usage of the Extended Kalman Filter on a low cost hardware. © 2018

关键词： Extended Kalman filters Acceleration Data handling Electric drives Filtration Mobile robots Nonlinear systems sensor data fusion Design and implementations Digital implementation Low cost hardware Mechanical systems Processing problems sensor fusion algorithms smoothing Walking robots

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Clean-Breathing: a Novel sensor fusion Algorithm Based on ICA to Remove Motion Artifacts from Breathing Signal

Clean-Breathing: a Novel Sensor Fusion Algorithm Based on IC...

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IEEE International Workshop on Metrology for Industry 4.0 and IoT (METROIND4.O and IoT)

作者： Raiano, Luigi Di Tocco, Joshua Massaroni, Carlo Di Pino, Giovanni Schena, Emiliano Formica, Domenico Univ Campus Biomed Roma NeXT Lab Rome Italy Univ Campus Biomed Roma Unit Measurements Rome Italy

ISBN: (纸本)9781728148922

Although smart-textile solutions based on piezoresistive technology have emerged as a tool to assess unobtrusively breathing activity, their signals are affected by the artifacts related to the subjects' movements during common activities (e.g. walking or running) In order to remove such artifacts, we implemented a novel algorithm combining the information recorded by four piezoresistive textile sensors, which allowed to measure rib cage movements due to the breathing activity, with the data synchronously recorded by an inertial measurement unit. Specifically, by using an Independent Component Analysis (ICA), our algorithm allowed to blindly reduce movement artifacts from the signals recorded by the piezo sensors, leading to highlight the breathing activity. We tested our algorithm in a pilot study, in which we enrolled one healthy subject during a free-running task. In order to assess our approach, we compared the signal spectrum obtained applying our algorithm with the one computed after a standard band-pass filter at 0.05-3 Hz. To this aim, we compared the average amplitude of the Power Spectral Densities (PSDs), computed after both approaches, along three frequency ranges: i) [0-1] Hz, related to the breathing activity;ii) [1-2] Hz, related with the torso rotation, during a running a task;iii) [2-3] Hz, related with the pace, during a running a task. Although the study was performed on one single subject, the results obtained seem to be promising. Indeed, within the range [0-1] Hz, the average reduction of the Power Spectral Density (PSD) is only about 4%, while it is considerably higher within frequency range related to the walking/running activity. Specifically, considering the ranges [1-2] Hz and [2-3] Hz such a reduction is around 39% and 36% respectively.

关键词： Respiratory Frequency Assessment Smart Textile Independent Component Analysis sensor fusion algorithms

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Innovative Exploration of a Bio-Inspired sensor fusion Algorithm: Enhancing Micro Satellite Functionality through Touretsky's Decentralized Neural Networks

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2024年第2期110卷 60-60页

作者： Hassani, S. M. Mehdi N. Roshanian, Jafar KN Toosi Univ Technol Inst Intelligent Control Syst Tehran Iran

Insect-inspired sensor fusion algorithms have presented a promising avenue in the development of robust and efficient systems, owing to the insects' ability to process numerous streams of noisy sensory data. The ring attractor neural network architecture has been identified as a noteworthy model for the optimal integration of diverse insect sensors. Expanding on this, our research presents an innovative bio-inspired ring attractor neural network architecture designed to augment the performance of microsatellite attitude determination systems through the fusion of data from multiple gyroscopic *** simulations using a nonlinear model of the microsatellite, while incorporating specific navigational disturbances, have been conducted to ascertain the viability and effectiveness of this approach. The results obtained have been superior to those of alternative methodologies, thus highlighting the potential of our proposed bio-inspired fusion technique. The findings indicate that this approach could significantly improve the accuracy and robustness of microsatellite systems across a wide range of applications.

关键词： Bio-inspired navigation Ring attractor neural network Touretsky neural network sensor fusion algorithms Insect Path Integration

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Posture Estimation in Agriculture: Employing Inertial Measurement Units and Unscented Kalman Filtering for Trunk, Shoulder, and Elbow Analysis

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IEEE ACCESS 2024年 12卷 97332-97345页

作者： Zougali, Amine Thamsuwan, Ornwipa Ecole Technol Super Dept Mech Engn Montreal PQ H3C 1K3 Canada

Farmworkers are often at risk of musculoskeletal health problems, with low back pain being the most common, accounting for approximately over half of the population worldwide. Moreover, these musculoskeletal disorders (MSD) are prevalent in other body regions including shoulders, elbows, wrists or hands. As these issues were found related to inappropriate working postures, it is beneficial to quantify these postures in order to prevent work-related MSD. The primary focus of this study was to improve the estimation of the postures and the exposure to non-neutral postures among agricultural workers. Three inertial measurement units (IMU) were attached to the upper back, upper arm, and forearm of nine workers while they were performing their regular work activities. A posture characterization algorithm was developed to rely on the data from only accelerometers and gyroscopes while excluding magnetometer readings due to high magnetic disturbance. Despite these challenges, a specialized unscented Kalman filter (UKF) was developed to achieve a more precise posture estimation. The UKF effectively expanded the range of pitch angles from +/- 90 degrees to +/- 180 degrees, resulting in a substantial improvement in the assessment of the back inclination as well as shoulder and elbow angles. The study was carried out among workers in a large-scale plant nursery, revealing instances of extreme postures in the back, upper arms, and elbows during their work activities. The quantitative findings highlighted the high exposure to ergonomic risks faced by the workers. This emphasized the urgent need for implementing appropriate measures to mitigate these risks.

关键词： Gyroscopes Estimation Accelerometers Kalman filters Magnetometers Elbow Task analysis Agriculture sensor fusion Musculoskeletal system Farming Human factors Pain inertial sensor human posture sensor fusion algorithms unscented Kalman filter

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A Full-State Robust Extended Kalman Filter for Orientation Tracking During Long-Duration Dynamic Tasks Using Magnetic and Inertial Measurement Units

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2021年 29卷 1280-1289页

作者： Nazarahari, Milad Rouhani, Hossein Univ Alberta Dept Mech Engn Neuromuscular Control & Biomech Lab Edmonton AB T6G 2R3 Canada

Accurate and robust orientation estimation using magnetic and inertial measurement units (MIMUs) has been a challenge for many years in long-duration measurements of joint angles and pedestrian dead-reckoning systems and has limited several real-world applications of MIMUs. Thus, this research aimed at developing a full-state Robust Extended Kalman Filter (REKF) for accurate and robust orientation tracking with MIMUs, particularly during long-duration dynamic tasks. First, we structured a novel EKF by including the orientation quaternion, non-gravitational acceleration, gyroscope bias, and magnetic disturbance in the state vector. Next, the a posteriori error covariance matrix equation was modified to build a REKF. We compared the accuracy and robustness of our proposed REKF with four filters from the literature using optimal filter gains. We measured the thigh, shank, and foot orientation of nine participants while performing short- and long-duration tasks using MIMUs and a camera motion-capture system. REKF outperformed the filters from literature significantly (p < 0.05) in terms of accuracy and robustness for long-duration tasks. For example, for foot MIMU, the median RMSE of (roll, pitch, yaw) were (6.5, 5.5, 7.8) and (22.8, 23.9, 25) deg for REKF and the best filter from the literature, respectively. For short-duration trials, REKF achieved significantly (p < 0.05) better or similar performance compared to the literature. We concluded that including non-gravitational acceleration, gyroscope bias, and magnetic disturbance in the state vector, as well as using a robust filter structure, is required for accurate and robust orientation tracking, at least in long-duration tasks.

关键词： Magnetic separation Magnetometers Mathematical model Gyroscopes Magnetic field measurement Task analysis Accelerometers Human motion monitoring body sensor network inertial navigation systems robust extended Kalman filter sensor fusion algorithms

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Clean-Breathing: a Novel sensor fusion Algorithm Based on ICA to Remove Motion Artifacts from Breathing Signal

Clean-Breathing: a Novel Sensor Fusion Algorithm Based on IC...

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Workshop on Metrology for Industry 4.0 and IoT

作者： Luigi Raiano Joshua Di Tocco Carlo Massaroni Giovanni Di Pino Emiliano Schena Domenico Formica Università Campus Bio-Medico di Roma NeXT Lab Rome Italy Università Campus Bio-Medico di Roma Unit of Measurements Rome Italy

ISBN: (数字)9781728148922

ISBN: (纸本)9781728148939

Although smart-textile solutions based on piezoresistive technology have emerged as a tool to assess unobtrusively breathing activity, their signals are affected by the artifacts related to the subjects' movements during common activities (e.g. walking or running). In order to remove such artifacts, we implemented a novel algorithm combining the information recorded by four piezoresistive textile sensors, which allowed to measure rib cage movements due to the breathing activity, with the data synchronously recorded by an inertial measurement unit. Specifically, by using an Independent Component Analysis (ICA), our algorithm allowed to blindly reduce movement artifacts from the signals recorded by the piezo sensors, leading to highlight the breathing activity. We tested our algorithm in a pilot study, in which we enrolled one healthy subject during a free-running task. In order to assess our approach, we compared the signal spectrum obtained applying our algorithm with the one computed after a standard band-pass filter at 0.05-3 Hz. To this aim, we compared the average amplitude of the Power Spectral Densities (PSDs), computed after both approaches, along three frequency ranges: i) [0-1] Hz, related to the breathing activity; ii) [1-2] Hz, related with the torso rotation, during a running a task; iii) [2-3] Hz, related with the pace, during a running a task. Although the study was performed on one single subject, the results obtained seem to be promising. Indeed, within the range [0-1] Hz, the average reduction of the Power Spectral Density (PSD) is only about 4%, while it is considerably higher within frequency range related to the walking/running activity. Specifically, considering the ranges [1-2] Hz and [2-3] Hz such a reduction is around 39% and 36% respectively.

关键词： Respiratory Frequency Assessment Smart Textile Independent Component Analysis sensor fusion algorithms Independent component analysis Smart textiles Hz respiration islet cell antibody algorithms signal spectrum Motion artifacts signals frequency ranges activity sensor fusion Pilot Studies

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Experimentally Validated Extended Kalman Filter for UAV State Estimation Using Low-Cost sensors

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IFAC-PapersOnLine 2018年第15期51卷 43-48页

作者： Driessen, S.P.H. Janssen, N.H.J. Wang, L. Palmer, J.L. Nijmeijer, H. Eindhoven University of Technology Den Dolech 2 EindhovenMB5600 Netherlands RMIT University 124 La Trobe Street MelbourneVIC3000 Australia DST Group 506 Lorimer Street Port MelbourneVIC3207 Australia

Visually based velocity and position estimations are often used to reduce or remove the dependency of an unmanned aerial vehicle (UAV) on global navigation satellite system signals, which may be unreliable in urban canyons and are unavailable indoors. In this paper, a sensor-fusion algorithm based on an extended Kalman filter is developed for the velocity, position, and attitude estimation of a UAV using low-cost sensors. In particular an inertial measurement unit (IMU) and an optical-flow sensor that includes a sonar module and an additional gyroscope are used. The algorithm is shown experimentally to be able to handle measurements with different sampling rates and missing data, caused by the indoor, low-light conditions. State estimations are compared to a ground-truth pose history obtained with a motion-capture system to show the influence of the optical-flow and sonar measurements on its performance. Additionally, the experimental results demonstrate that the velocity and attitude can be estimated without drift, despite the magnetic distortions typical of indoor environments. © 2018

关键词： Extended Kalman filters Air navigation Antennas Cost estimating Optical flows sensor data fusion Sonar State estimation Unmanned aerial vehicles (UAV) Global Navigation Satellite Systems Inertial Measurement Unit (IMU) Magnetic distortions Missing data Motion capture system Sampled data sensor fusion sensor fusion algorithms

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