Tracking Using an Inertial Measurement Unit (IMU) and GPS

2012 ◽  
Vol 229-231 ◽  
pp. 1469-1475 ◽  
Author(s):  
Hussein M. Magboub ◽  
Mohamed A. Msallem ◽  
Nasser Ali
Keyword(s):  
Inertial Measurement Unit ◽  
Simulation Software ◽  
Measurement Unit ◽  
Matlab Simulation ◽  
Tracking Accuracy ◽  
Inertial Measurement ◽  
Wide Range ◽  
Different Types ◽  
Significant Performance ◽  
Significant Attention

Since the last decade, vehicle tracking has been attracting significant attention in a wide range of applications. To deliver on their requirements, these applications need a specific tracking accuracy. However, current tracking techniques lack the required accuracy, especially for mission critical applications. Although these techniques have demonstrated significant performance improvement, there remain situations that give rise to degraded tracking accuracy, a deficiency that many applications cannot tolerate. This has motivated the research and development of advanced tracking. In this paper will be the design and implementation of an inertial navigation system (INS) using an inertial measurement unit (IMU) and GPS by Matlab simulation software. The INS is capable of providing continuous estimates of a vehicle’s position and orientation. And Comparative study of different types of estimation filters (KF, EKF) which has high accuracy is used to improve system state estimation.

scholarly journals Analysis of the Accuracy of Ten Algorithms for Orientation Estimation Using Inertial and Magnetic Sensing Under Optimal Conditions: One Size Does Not Fit All

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2543
Author(s):  
Marco Caruso ◽  
Angelo Maria Sabatini ◽  
Daniel Laidig ◽  
Thomas Seel ◽  
Marco Knaflitz ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Motion Tracking ◽  
Recent Literature ◽  
Human Motion ◽  
Measurement Unit ◽  
Optimal Conditions ◽  
Inertial Measurement ◽  
Rotation Rates ◽  
Significant Performance ◽  
Parameter Values

The orientation of a magneto and inertial measurement unit (MIMU) is estimated by means of sensor fusion algorithms (SFAs) thus enabling human motion tracking. However, despite several SFAs implementations proposed over the last decades, there is still a lack of consensus about the best performing SFAs and their accuracy. As suggested by recent literature, the filter parameters play a central role in determining the orientation errors. The aim of this work is to analyze the accuracy of ten SFAs while running under the best possible conditions (i.e., their parameter values are set using the orientation reference) in nine experimental scenarios including three rotation rates and three commercial products. The main finding is that parameter values must be specific for each SFA according to the experimental scenario to avoid errors comparable to those obtained when the default parameter values are used. Overall, when optimally tuned, no statistically significant differences are observed among the different SFAs in all tested experimental scenarios and the absolute errors are included between 3.8 deg and 7.1 deg. Increasing the rotation rate generally leads to a significant performance worsening. Errors are also influenced by the MIMU commercial model. SFA MATLAB implementations have been made available online.

scholarly journals A Modified Kalman Filter for Integrating the Different Rate Data of Gyros and Accelerometers Retrieved from Android Smartphones in the GNSS/IMU Coupled Navigation

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5208
Author(s):  
Wenlin Yan ◽  
Qiuzhao Zhang ◽  
Lijuan Wang ◽  
Ying Mao ◽  
Aisheng Wang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Rate Data ◽  
Slight Improvement ◽  
Inertial Measurement ◽  
Position Accuracy ◽  
Straight Line ◽  
Different Types ◽  
Modified Kalman Filter

Recent study indicates that by using the inertial measurement unit (IMU) sensors inside smartphones, we can obtain similar navigation solutions to the professional ones. However, the sampling rates of the gyros and accelerometers inside some types of smartphones are not set in the same frequencies, i.e., the gyros of “Huawei p40” are in 50 Hz while the accelerometer is 100 Hz. The conventional method is resampling the higher frequency to the lower frequency ones, which means the resampled accelerometer will lose half frequency observations. In this work, a modified Kalman filter was proposed to integrate all these different rate IMU data in the GNSS/IMU-smartphone coupled navigation. To validate the proposed method, a terrestrial test with two different types of android smartphones was done. With the proposed method, a slight improvement of the attitude solutions can be seen in the experiments under the GNSS open-sky condition, and the obvious improvement of the attitude solutions can be witnessed at the simulated GNSS denied situation. The improvements by 45% and 23% of the horizontal position accuracy can be obtained from the experiments under the GNSS outage of 50 s in a straight line and 30 s in a turning line, respectively.

scholarly journals Towards Human Motion Tracking: Multi-Sensory IMU/TOA Fusion Method and Fundamental Limits

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 142 ◽  
Author(s):  
Cheng Xu ◽  
Jie He ◽  
Xiaotong Zhang ◽  
Xinghang Zhou ◽  
Shihong Duan
Keyword(s):  
Inertial Measurement Unit ◽  
Motion Tracking ◽  
Human Motion ◽  
Measurement Unit ◽  
Time Of Arrival ◽  
Fusion Method ◽  
Tracking Accuracy ◽  
Fundamental Limits ◽  
Inertial Measurement ◽  
Human Motion Tracking

Human motion tracking could be viewed as a multi-target tracking problem towards numerous body joints. Inertial-measurement-unit-based human motion tracking technique stands out and has been widely used in body are network applications. However, it has been facing the tough problem of accumulative errors and drift. In this paper, we propose a multi-sensor hybrid method to solve this problem. Firstly, an inertial-measurement-unit and time-of-arrival fusion-based method is proposed to compensate the drift and accumulative errors caused by inertial sensors. Secondly, Cramér–Rao lower bound is derived in detail with consideration of both spatial and temporal related factors. Simulation results show that the proposed method in this paper has both spatial and temporal advantages, compared with traditional sole inertial or time-of-arrival-based tracking methods. Furthermore, proposed method is verified in 3D practical application scenarios. Compared with state-of-the-art algorithms, proposed fusion method shows better consistency and higher tracking accuracy, especially when moving direction changes. The proposed fusion method and comprehensive fundamental limits analysis conducted in this paper can provide a theoretical basis for further system design and algorithm analysis. Without the requirements of external anchors, the proposed method has good stability and high tracking accuracy, thus it is more suitable for wearable motion tracking applications.

scholarly journals Can Machine Learning with IMUs Be Used to Detect Different Throws and Estimate Ball Velocity in Team Handball?

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2288
Author(s):  
Roland van den Tillaar ◽  
Shruti Bhandurge ◽  
Tom Stewart
Keyword(s):  
Machine Learning ◽  
Injury Prevention ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Prevention Strategies ◽  
Inertial Measurement ◽  
Automated Method ◽  
Team Handball ◽  
Different Types ◽  
Ball Velocity

Injuries in handball are common due to the repetitive demands of overhead throws at high velocities. Monitoring workload is crucial for understanding these demands and improving injury-prevention strategies. However, in handball, it is challenging to monitor throwing workload due to the difficulty of counting the number, intensity, and type of throws during training and competition. The aim of this study was to investigate if an inertial measurement unit (IMU) and machine learning (ML) techniques could be used to detect different types of team handball throws and predict ball velocity. Seventeen players performed several throws with different wind-up (circular and whip-like) and approach types (standing, running, and jumping) while wearing an IMU on their wrist. Ball velocity was measured using a radar gun. ML models predicted peak ball velocity with an error of 1.10 m/s and classified approach type and throw type with 80–87% accuracy. Using IMUs and ML models may offer a practical and automated method for quantifying throw counts and classifying the throw and approach types adopted by handball players.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

scholarly journals Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4767
Author(s):  
Karla Miriam Reyes Leiva ◽  
Milagros Jaén-Vargas ◽  
Benito Codina ◽  
José Javier Serrano Olmedo
Keyword(s):  
Inertial Measurement Unit ◽  
Visually Impaired ◽  
Inertial Sensors ◽  
Assistive Technologies ◽  
Biomechanical Analysis ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Visually Impaired People ◽  
Impaired People ◽  
Application Fields

A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

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