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Special Issue "Inertial Sensors"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 December 2019).

Special Issue Editors

Dr. Thomas Seel
Website
Guest Editor
Control Systems Group, Technische Universität Berlin, Berlin 10587, Germany
Interests: methods for state estimation, sensor fusion, and learning control; inertial sensor networks; applications in rehabilitation, vehicle, and energy systems
Dr. Manon Kok
Website
Guest Editor
Delft Center for Systems and Control, Delft University of Technology, Delft, Netherlands
Interests: probabilistic modelling; sensor fusion; signal processing; machine learning; inertial sensors; magnetometers; indoor localisation; inertial motion capture
Dr. Ryan S. McGinnis
Website
Guest Editor
Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA
Interests: signal processing; machine learning; biomechanics; computational dynamics; development of digital biomarkers, phenotypes, and therapeutics
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Inertial sensors have become a key enabling technology in a large variety of applications, and are currently found in almost every digital device and every intelligent vehicle. Inertial sensor information is, for instance, combined with absolute position measurements to facilitate localization, navigation, and mapping. Wearable inertial sensors are also frequently used in health care and sports applications for capturing movement patterns outside of typical laboratory and clinical environments. Many other application domains and examples can be found.

At the same time, inertial measurements are known to be corrupted by errors such as sensor bias, noise, and misalignment. Because of this, integration of the sensor signals to position and orientation results in integration drift. Inertial sensor measurements are therefore typically combined with additional information, such as from additional sensor measurements or additional models. Advanced sensor fusion and state estimation methods are being developed to combine this information and minimize the influence of the sensor errors on the motion states of interest. Statistical filters and state estimation techniques are commonly used. Machine learning methods have recently emerged as a promising new direction.

This Special Issue aims to gather novel developments in the use of inertial sensors, including both recent methodological developments and new results in applications of inertial sensors. Given the focus on methodological developments, we strongly encourage authors to deposit their source code in a public repository (e.g., GitHub) if possible. Topics include but are not limited to the following keywords.

Dr. Thomas Seel
Dr. Manon Kok
Dr. Ryan McGinnis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • accelerometers, gyroscopes, magnetometers
  • sensor fusion algorithms
  • body area networks
  • inertial motion tracking
  • error modelling and calibration
  • localization and mapping, SLAM
  • machine learning applied to inertial sensor data
  • inertial sensors in vehicle motion estimation and control
  • inertial sensors in robotics and manufacturing
  • inertial sensors in health care and sports engineering

Published Papers (14 papers)

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Research

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Open AccessArticle
Human-In-The-Loop Assessment of an Ultralight, Low-Cost Body Posture Tracking Device
Sensors 2020, 20(3), 890; https://doi.org/10.3390/s20030890 - 07 Feb 2020
Abstract
In rehabilitation, assistive and space robotics, the capability to track the body posture of a user in real time is highly desirable. In more specific cases, such as teleoperated extra-vehicular activity, prosthetics and home service robotics, the ideal posture-tracking device must also be [...] Read more.
In rehabilitation, assistive and space robotics, the capability to track the body posture of a user in real time is highly desirable. In more specific cases, such as teleoperated extra-vehicular activity, prosthetics and home service robotics, the ideal posture-tracking device must also be wearable, light and low-power, while still enforcing the best possible accuracy. Additionally, the device must be targeted at effective human-machine interaction. In this paper, we present and test such a device based upon commercial inertial measurement units: it weighs 575 g in total, lasts up to 10.5 h of continual operation, can be donned and doffed in under a minute and costs less than 290 EUR. We assess the attainable performance in terms of error in an online trajectory-tracking task in Virtual Reality using the device through an experiment involving 10 subjects, showing that an average user can attain a precision of 0.66 cm during a static precision task and 6.33 cm while tracking a moving trajectory, when tested in the full peri-personal space of a user. Full article
(This article belongs to the Special Issue Inertial Sensors)
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Open AccessArticle
A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots
Sensors 2020, 20(3), 803; https://doi.org/10.3390/s20030803 - 01 Feb 2020
Cited by 2
Abstract
This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which [...] Read more.
This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy. Full article
(This article belongs to the Special Issue Inertial Sensors)
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Open AccessArticle
Overcoming Bandwidth Limitations in Wireless Sensor Networks by Exploitation of Cyclic Signal Patterns: An Event-triggered Learning Approach
Sensors 2020, 20(1), 260; https://doi.org/10.3390/s20010260 - 02 Jan 2020
Abstract
Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors. This problem can be addressed by reducing the communication load [...] Read more.
Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors. This problem can be addressed by reducing the communication load via data compression and event-based communication approaches. The present paper focuses on the class of applications in which the signals exhibit unknown and potentially time-varying cyclic patterns. We review recently proposed event-triggered learning (ETL) methods that identify and exploit these cyclic patterns, we show how these methods can be applied to the nonlinear multivariable dynamics of three-dimensional orientation data, and we propose a novel approach that uses Gaussian process models. In contrast to other approaches, all three ETL methods work in real time and assure a small upper bound on the reconstruction error. The proposed methods are compared to several conventional approaches in experimental data from human subjects walking with a wearable inertial sensor network. They are found to reduce the communication load by 60–70%, which implies that two to three times more sensor nodes could be used at the same bandwidth. Full article
(This article belongs to the Special Issue Inertial Sensors)
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Open AccessArticle
Magnetic Condition-Independent 3D Joint Angle Estimation Using Inertial Sensors and Kinematic Constraints
Sensors 2019, 19(24), 5522; https://doi.org/10.3390/s19245522 - 13 Dec 2019
Abstract
In biomechanics, joint angle estimation using wearable inertial measurement units (IMUs) has been getting great popularity. However, magnetic disturbance issue is considered problematic as the disturbance can seriously degrade the accuracy of the estimated joint angles. This study proposes a magnetic condition-independent three-dimensional [...] Read more.
In biomechanics, joint angle estimation using wearable inertial measurement units (IMUs) has been getting great popularity. However, magnetic disturbance issue is considered problematic as the disturbance can seriously degrade the accuracy of the estimated joint angles. This study proposes a magnetic condition-independent three-dimensional (3D) joint angle estimation method based on IMU signals. The proposed method is implemented in a sequential direction cosine matrix-based orientation Kalman filter (KF), which is composed of an attitude estimation KF followed by a heading estimation KF. In the heading estimation KF, an acceleration-level kinematic constraint from a spherical joint replaces the magnetometer signals for the correction procedure. Because the proposed method does not rely on the magnetometer, it is completely magnetic condition-independent and is not affected by the magnetic disturbance. For the averaged root mean squared errors of the three tests performed using a rigid two-link system, the proposed method produced 1.58°, while the conventional method with the magnetic disturbance compensation mechanism produced 5.38°, showing a higher accuracy of the proposed method in the magnetically disturbed conditions. Due to the independence of the proposed method from the magnetic condition, the proposed approach could be reliably applied in various fields that require robust 3D joint angle estimation through IMU signals in an unspecified arbitrary magnetic environment. Full article
(This article belongs to the Special Issue Inertial Sensors)
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Open AccessArticle
Incremental Learning to Personalize Human Activity Recognition Models: The Importance of Human AI Collaboration
Sensors 2019, 19(23), 5151; https://doi.org/10.3390/s19235151 - 25 Nov 2019
Cited by 1
Abstract
This study presents incremental learning based methods to personalize human activity recognition models. Initially, a user-independent model is used in the recognition process. When a new user starts to use the human activity recognition application, personal streaming data can be gathered. Of course, [...] Read more.
This study presents incremental learning based methods to personalize human activity recognition models. Initially, a user-independent model is used in the recognition process. When a new user starts to use the human activity recognition application, personal streaming data can be gathered. Of course, this data does not have labels. However, there are three different ways to obtain this data: non-supervised, semi-supervised, and supervised. The non-supervised approach relies purely on predicted labels, the supervised approach uses only human intelligence to label the data, and the proposed method for semi-supervised learning is a combination of these two: It uses artificial intelligence (AI) in most cases to label the data but in uncertain cases it relies on human intelligence. After labels are obtained, the personalization process continues by using the streaming data and these labels to update the incremental learning based model, which in this case is Learn++. Learn++ is an ensemble method that can use any classifier as a base classifier, and this study compares three base classifiers: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and classification and regression tree (CART). Moreover, three datasets are used in the experiment to show how well the presented method generalizes on different datasets. The results show that personalized models are much more accurate than user-independent models. On average, the recognition rates are: 87.0% using the user-independent model, 89.1% using the non-supervised personalization approach, 94.0% using the semi-supervised personalization approach, and 96.5% using the supervised personalization approach. This means that by relying on predicted labels with high confidence, and asking the user to label only uncertain observations (6.6% of the observations when using LDA, 7.7% when using QDA, and 18.3% using CART), almost as low error rates can be achieved as by using the supervised approach, in which labeling is fully based on human intelligence. Full article
(This article belongs to the Special Issue Inertial Sensors)
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Open AccessArticle
Validation of Novel Relative Orientation and Inertial Sensor-to-Segment Alignment Algorithms for Estimating 3D Hip Joint Angles
Sensors 2019, 19(23), 5143; https://doi.org/10.3390/s19235143 - 24 Nov 2019
Cited by 1
Abstract
Wearable sensor-based algorithms for estimating joint angles have seen great improvements in recent years. While the knee joint has garnered most of the attention in this area, algorithms for estimating hip joint angles are less available. Herein, we propose and validate a novel [...] Read more.
Wearable sensor-based algorithms for estimating joint angles have seen great improvements in recent years. While the knee joint has garnered most of the attention in this area, algorithms for estimating hip joint angles are less available. Herein, we propose and validate a novel algorithm for this purpose with innovations in sensor-to-sensor orientation and sensor-to-segment alignment. The proposed approach is robust to sensor placement and does not require specific calibration motions. The accuracy of the proposed approach is established relative to optical motion capture and compared to existing methods for estimating relative orientation, hip joint angles, and range of motion (ROM) during a task designed to exercise the full hip range of motion (ROM) and fast walking using root mean square error (RMSE) and regression analysis. The RMSE of the proposed approach was less than that for existing methods when estimating sensor orientation ( 12.32 ° and 11.82 ° vs. 24.61 ° and 23.76 ° ) and flexion/extension joint angles ( 7.88 ° and 8.62 ° vs. 14.14 ° and 15.64 ° ). Also, ROM estimation error was less than 2.2 ° during the walking trial using the proposed method. These results suggest the proposed approach presents an improvement to existing methods and provides a promising technique for remote monitoring of hip joint angles. Full article
(This article belongs to the Special Issue Inertial Sensors)
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Open AccessArticle
Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training
Sensors 2019, 19(21), 4804; https://doi.org/10.3390/s19214804 - 05 Nov 2019
Cited by 4
Abstract
Technologies such as robot-assisted gait trainers or functional electrical stimulation can improve the rehabilitation process of people affected with gait disorders due to stroke or other neurological defects. By combining both technologies, the potential disadvantages of each technology could be compensated and simultaneously, [...] Read more.
Technologies such as robot-assisted gait trainers or functional electrical stimulation can improve the rehabilitation process of people affected with gait disorders due to stroke or other neurological defects. By combining both technologies, the potential disadvantages of each technology could be compensated and simultaneously, therapy effects could be improved. Thus, an algorithm was designed that aims to detect the gait cycle of a robot-assisted gait trainer. Based on movement data recorded with inertial measurement units, gait events can be detected. These events can further be used to trigger functional electrical stimulation. This novel setup offers the possibility of equipping a broad range of potential robot-assisted gait trainers with functional electrical stimulation. The aim of this paper in particular was to test the feasibility of a system using inertial measurement units for gait event detection during robot-assisted gait training. Thus, a 39-year-old healthy male adult executed a total of six training sessions with two robot-assisted gait trainers (Lokomat and Lyra). The measured data from the sensors were analyzed by a custom-made gait event detection algorithm. An overall detection rate of 98.1% ± 5.2% for the Lokomat and 94.1% ± 6.8% for the Lyra was achieved. The mean type-1 error was 0.3% ± 1.2% for the Lokomat and 1.9% ± 4.3% for the Lyra. As a result, the setup provides promising results for further research and a technique that can enhance robot-assisted gait trainers by adding functional electrical stimulation to the rehabilitation process. Full article
(This article belongs to the Special Issue Inertial Sensors)
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Open AccessArticle
IMU Sensor-Based Hand Gesture Recognition for Human-Machine Interfaces
Sensors 2019, 19(18), 3827; https://doi.org/10.3390/s19183827 - 04 Sep 2019
Cited by 4
Abstract
We propose an efficient hand gesture recognition (HGR) algorithm, which can cope with time-dependent data from an inertial measurement unit (IMU) sensor and support real-time learning for various human-machine interface (HMI) applications. Although the data extracted from IMU sensors are time-dependent, most existing [...] Read more.
We propose an efficient hand gesture recognition (HGR) algorithm, which can cope with time-dependent data from an inertial measurement unit (IMU) sensor and support real-time learning for various human-machine interface (HMI) applications. Although the data extracted from IMU sensors are time-dependent, most existing HGR algorithms do not consider this characteristic, which results in the degradation of recognition performance. Because the dynamic time warping (DTW) technique considers the time-dependent characteristic of IMU sensor data, the recognition performance of DTW-based algorithms is better than that of others. However, the DTW technique requires a very complex learning algorithm, which makes it difficult to support real-time learning. To solve this issue, the proposed HGR algorithm is based on a restricted column energy (RCE) neural network, which has a very simple learning scheme in which neurons are activated when necessary. By replacing the metric calculation of the RCE neural network with DTW distance, the proposed algorithm exhibits superior recognition performance for time-dependent sensor data while supporting real-time learning. Our verification results on a field-programmable gate array (FPGA)-based test platform show that the proposed HGR algorithm can achieve a recognition accuracy of 98.6% and supports real-time learning and recognition at an operating frequency of 150 MHz. Full article
(This article belongs to the Special Issue Inertial Sensors)
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Open AccessArticle
Estimation of Knee Joint Forces in Sport Movements Using Wearable Sensors and Machine Learning
Sensors 2019, 19(17), 3690; https://doi.org/10.3390/s19173690 - 25 Aug 2019
Cited by 16
Abstract
Knee joint forces (KJF) are biomechanical measures used to infer the load on knee joint structures. The purpose of this study is to develop an artificial neural network (ANN) that estimates KJF during sport movements, based on data obtained by wearable sensors. Thirteen [...] Read more.
Knee joint forces (KJF) are biomechanical measures used to infer the load on knee joint structures. The purpose of this study is to develop an artificial neural network (ANN) that estimates KJF during sport movements, based on data obtained by wearable sensors. Thirteen participants were equipped with two inertial measurement units (IMUs) located on the right leg. Participants performed a variety of movements, including linear motions, changes of direction, and jumps. Biomechanical modelling was carried out to determine KJF. An ANN was trained to model the association between the IMU signals and the KJF time series. The ANN-predicted KJF yielded correlation coefficients that ranged from 0.60 to 0.94 (vertical KJF), 0.64 to 0.90 (anterior–posterior KJF) and 0.25 to 0.60 (medial–lateral KJF). The vertical KJF for moderate running showed the highest correlation (0.94 ± 0.33). The summed vertical KJF and peak vertical KJF differed between calculated and predicted KJF across all movements by an average of 5.7% ± 5.9% and 17.0% ± 13.6%, respectively. The vertical and anterior–posterior KJF values showed good agreement between ANN-predicted outcomes and reference KJF across most movements. This study supports the use of wearable sensors in combination with ANN for estimating joint reactions in sports applications. Full article
(This article belongs to the Special Issue Inertial Sensors)
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Open AccessArticle
GAM-Based Mooring Alignment for SINS Based on An Improved CEEMD Denoising Method
Sensors 2019, 19(16), 3564; https://doi.org/10.3390/s19163564 - 15 Aug 2019
Cited by 2
Abstract
To solve the self-alignment problem of the Strapdown Inertial Navigation System (SINS), a novel adaptive filter based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) is proposed. The Gravitational Apparent Motion (GAM) is used in the coarse alignment, and the problem of obtaining the [...] Read more.
To solve the self-alignment problem of the Strapdown Inertial Navigation System (SINS), a novel adaptive filter based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) is proposed. The Gravitational Apparent Motion (GAM) is used in the coarse alignment, and the problem of obtaining the attitude matrix between the body frame and the navigation frame is attributed to obtaining the matrix between the initial body frame and the current navigation frame using two gravitational apparent motion vectors at different moments. However, the accuracy and time of this alignment method always suffer from the measurement noise of sensors. Thus, a novel adaptive filter based on CEEMD using an l 2 -norm to calculate the similarity measure between the Probability Density Function (PDF) of each Intrinsic Mode Function (IMF) and the original signal is proposed to denoise the measurements of the accelerometer. Furthermore, the advantage of this filter is verified by comparing with other conventional denoising methods, such as PDF-based EMD (EMD-PDF) and the Finite Impulse Response (FIR) digital low-pass filter method. The results of the simulation and experiments indicate that the proposed method performs better than the conventional methods in both alignment time and alignment accuracy. Full article
(This article belongs to the Special Issue Inertial Sensors)
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Open AccessArticle
Estimation of 3D Knee Joint Angles during Cycling Using Inertial Sensors: Accuracy of a Novel Sensor-to-Segment Calibration Procedure Based on Pedaling Motion
Sensors 2019, 19(11), 2474; https://doi.org/10.3390/s19112474 - 30 May 2019
Cited by 5
Abstract
This paper presents a novel sensor-to-segment calibration procedure for inertial sensor-based knee joint kinematics analysis during cycling. This procedure was designed to be feasible in-field, autonomously, and without any external operator or device. It combines a static standing up posture and a pedaling [...] Read more.
This paper presents a novel sensor-to-segment calibration procedure for inertial sensor-based knee joint kinematics analysis during cycling. This procedure was designed to be feasible in-field, autonomously, and without any external operator or device. It combines a static standing up posture and a pedaling task. The main goal of this study was to assess the accuracy of the new sensor-to-segment calibration method (denoted as the ‘cycling’ method) by calculating errors in terms of body-segment orientations and 3D knee joint angles using inertial measurement unit (IMU)-based and optoelectronic-based motion capture. To do so, 14 participants were evaluated during pedaling motion at a workload of 100 W, which enabled comparisons of the cycling method with conventional calibration methods commonly employed in gait analysis. The accuracy of the cycling method was comparable to that of other methods concerning the knee flexion/extension angle, and did not exceed 3.8°. However, the cycling method presented the smallest errors for knee internal/external rotation (6.65 ± 1.94°) and abduction/adduction (5.92 ± 2.85°). This study demonstrated that a calibration method based on the completion of a pedaling task combined with a standing posture significantly improved the accuracy of 3D knee joint angle measurement when applied to cycling analysis. Full article
(This article belongs to the Special Issue Inertial Sensors)
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Open AccessArticle
RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots
Sensors 2019, 19(10), 2251; https://doi.org/10.3390/s19102251 - 15 May 2019
Cited by 2
Abstract
Using camera sensors for ground robot Simultaneous Localization and Mapping (SLAM) has many benefits over laser-based approaches, such as the low cost and higher robustness. RGBD sensors promise the best of both worlds: dense data from cameras with depth information. This paper proposes [...] Read more.
Using camera sensors for ground robot Simultaneous Localization and Mapping (SLAM) has many benefits over laser-based approaches, such as the low cost and higher robustness. RGBD sensors promise the best of both worlds: dense data from cameras with depth information. This paper proposes to fuse RGBD and IMU data for a visual SLAM system, called VINS-RGBD, that is built upon the open source VINS-Mono software. The paper analyses the VINS approach and highlights the observability problems. Then, we extend the VINS-Mono system to make use of the depth data during the initialization process as well as during the VIO (Visual Inertial Odometry) phase. Furthermore, we integrate a mapping system based on subsampled depth data and octree filtering to achieve real-time mapping, including loop closing. We provide the software as well as datasets for evaluation. Our extensive experiments are performed with hand-held, wheeled and tracked robots in different environments. We show that ORB-SLAM2 fails for our application and see that our VINS-RGBD approach is superior to VINS-Mono. Full article
(This article belongs to the Special Issue Inertial Sensors)
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Open AccessArticle
MIMO Fuzzy Sliding Mode Control for Three-Axis Inertially Stabilized Platform
Sensors 2019, 19(7), 1658; https://doi.org/10.3390/s19071658 - 06 Apr 2019
Cited by 1
Abstract
In this paper, a MIMO (Multi-Input Multi-Output) fuzzy sliding mode control method is proposed for a three-axis inertially stabilized platform. This method is based on the MIMO coupling model of the three-axis inertially stabilized platform in which the dynamic coupling among the three [...] Read more.
In this paper, a MIMO (Multi-Input Multi-Output) fuzzy sliding mode control method is proposed for a three-axis inertially stabilized platform. This method is based on the MIMO coupling model of the three-axis inertially stabilized platform in which the dynamic coupling among the three frames, namely the azimuth frame, the pitch frame and the roll frame, is fully considered. Firstly, the dynamic equation of the three-axis inertially stabilized platform is analyzed and its linearized model is obtained. After this, the controller is designed based on the model, during which fuzzy logic is introduced to deal with the frame coupling and the adaptive fuzzy coupling compensation factor is designed to be part of the algorithm. A complete proof of the stability and convergence is also provided in this paper. Finally, the performance of the platform with a MIMO fuzzy sliding mode controller and PI controller is analyzed. The simulation results show that the proposed scheme can guarantee tracking accuracy and effectively suppress the coupling interference between the three frames. Full article
(This article belongs to the Special Issue Inertial Sensors)
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Review

Jump to: Research

Open AccessEditor’s ChoiceReview
Inertial Sensor-Based Lower Limb Joint Kinematics: A Methodological Systematic Review
Sensors 2020, 20(3), 673; https://doi.org/10.3390/s20030673 - 26 Jan 2020
Cited by 6
Abstract
The use of inertial measurement units (IMUs) has gained popularity for the estimation of lower limb kinematics. However, implementations in clinical practice are still lacking. The aim of this review is twofold—to evaluate the methodological requirements for IMU-based joint kinematic estimation to be [...] Read more.
The use of inertial measurement units (IMUs) has gained popularity for the estimation of lower limb kinematics. However, implementations in clinical practice are still lacking. The aim of this review is twofold—to evaluate the methodological requirements for IMU-based joint kinematic estimation to be applicable in a clinical setting, and to suggest future research directions. Studies within the PubMed, Web Of Science and EMBASE databases were screened for eligibility, based on the following inclusion criteria: (1) studies must include a methodological description of how kinematic variables were obtained for the lower limb, (2) kinematic data must have been acquired by means of IMUs, (3) studies must have validated the implemented method against a golden standard reference system. Information on study characteristics, signal processing characteristics and study results was assessed and discussed. This review shows that methods for lower limb joint kinematics are inherently application dependent. Sensor restrictions are generally compensated with biomechanically inspired assumptions and prior information. Awareness of the possible adaptations in the IMU-based kinematic estimates by incorporating such prior information and assumptions is necessary, before drawing clinical decisions. Future research should focus on alternative validation methods, subject-specific IMU-based biomechanical joint models and disturbed movement patterns in real-world settings. Full article
(This article belongs to the Special Issue Inertial Sensors)
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