Benchmarking Dataset of Signals from a Commercial MEMS Magnetic–Angular Rate–Gravity (MARG) Sensor Manipulated in Regions with and without Geomagnetic Distortion
In this paper, we present the FIU MARG Dataset (FIUMARGDB) of signals from the tri-axial accelerometer, gyroscope, and magnetometer contained in a low-cost miniature magnetic–angular rate–gravity (MARG) sensor module (also known as magnetic inertial measurement unit, MIMU) for the evaluation of MARG...
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/8/3786 |
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| author | Pontakorn Sonchan Neeranut Ratchatanantakit Nonnarit O-larnnithipong Malek Adjouadi Armando Barreto |
| author_facet | Pontakorn Sonchan Neeranut Ratchatanantakit Nonnarit O-larnnithipong Malek Adjouadi Armando Barreto |
| author_sort | Pontakorn Sonchan |
| collection | DOAJ |
| description | In this paper, we present the FIU MARG Dataset (FIUMARGDB) of signals from the tri-axial accelerometer, gyroscope, and magnetometer contained in a low-cost miniature magnetic–angular rate–gravity (MARG) sensor module (also known as magnetic inertial measurement unit, MIMU) for the evaluation of MARG orientation estimation algorithms. The dataset contains 30 files resulting from different volunteer subjects executing manipulations of the MARG in areas with and without magnetic distortion. Each file also contains reference (“ground truth”) MARG orientations (as quaternions) determined by an optical motion capture system during the recording of the MARG signals. The creation of FIUMARGDB responds to the increasing need for the objective comparison of the performance of MARG orientation estimation algorithms, using the same inputs (accelerometer, gyroscope, and magnetometer signals) recorded under varied circumstances, as MARG modules hold great promise for human motion tracking applications. This dataset specifically addresses the need to study and manage the degradation of orientation estimates that occur when MARGs operate in regions with known magnetic field distortions. To our knowledge, no other dataset with these characteristics is currently available. FIUMARGDB can be accessed through the URL indicated in the conclusions section. It is our hope that the availability of this dataset will lead to the development of orientation estimation algorithms that are more resilient to magnetic distortions, for the benefit of fields as diverse as human–computer interaction, kinesiology, motor rehabilitation, etc. |
| first_indexed | 2024-03-11T04:34:35Z |
| format | Article |
| id | doaj.art-ff2479fdf86c465dbb4999cc22dcf207 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:34:35Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ff2479fdf86c465dbb4999cc22dcf2072023-11-17T21:14:34ZengMDPI AGSensors1424-82202023-04-01238378610.3390/s23083786Benchmarking Dataset of Signals from a Commercial MEMS Magnetic–Angular Rate–Gravity (MARG) Sensor Manipulated in Regions with and without Geomagnetic DistortionPontakorn Sonchan0Neeranut Ratchatanantakit1Nonnarit O-larnnithipong2Malek Adjouadi3Armando Barreto4Electrical and Computer Engineering Department, Florida International University, Miami, FL 33174, USAElectrical and Computer Engineering Department, Florida International University, Miami, FL 33174, USAElectrical and Computer Engineering Department, Florida International University, Miami, FL 33174, USAElectrical and Computer Engineering Department, Florida International University, Miami, FL 33174, USAElectrical and Computer Engineering Department, Florida International University, Miami, FL 33174, USAIn this paper, we present the FIU MARG Dataset (FIUMARGDB) of signals from the tri-axial accelerometer, gyroscope, and magnetometer contained in a low-cost miniature magnetic–angular rate–gravity (MARG) sensor module (also known as magnetic inertial measurement unit, MIMU) for the evaluation of MARG orientation estimation algorithms. The dataset contains 30 files resulting from different volunteer subjects executing manipulations of the MARG in areas with and without magnetic distortion. Each file also contains reference (“ground truth”) MARG orientations (as quaternions) determined by an optical motion capture system during the recording of the MARG signals. The creation of FIUMARGDB responds to the increasing need for the objective comparison of the performance of MARG orientation estimation algorithms, using the same inputs (accelerometer, gyroscope, and magnetometer signals) recorded under varied circumstances, as MARG modules hold great promise for human motion tracking applications. This dataset specifically addresses the need to study and manage the degradation of orientation estimates that occur when MARGs operate in regions with known magnetic field distortions. To our knowledge, no other dataset with these characteristics is currently available. FIUMARGDB can be accessed through the URL indicated in the conclusions section. It is our hope that the availability of this dataset will lead to the development of orientation estimation algorithms that are more resilient to magnetic distortions, for the benefit of fields as diverse as human–computer interaction, kinesiology, motor rehabilitation, etc.https://www.mdpi.com/1424-8220/23/8/3786MARGMIMUorientation estimationsensor fusion algorithmdatasetorientation algorithm benchmarking |
| spellingShingle | Pontakorn Sonchan Neeranut Ratchatanantakit Nonnarit O-larnnithipong Malek Adjouadi Armando Barreto Benchmarking Dataset of Signals from a Commercial MEMS Magnetic–Angular Rate–Gravity (MARG) Sensor Manipulated in Regions with and without Geomagnetic Distortion Sensors MARG MIMU orientation estimation sensor fusion algorithm dataset orientation algorithm benchmarking |
| title | Benchmarking Dataset of Signals from a Commercial MEMS Magnetic–Angular Rate–Gravity (MARG) Sensor Manipulated in Regions with and without Geomagnetic Distortion |
| title_full | Benchmarking Dataset of Signals from a Commercial MEMS Magnetic–Angular Rate–Gravity (MARG) Sensor Manipulated in Regions with and without Geomagnetic Distortion |
| title_fullStr | Benchmarking Dataset of Signals from a Commercial MEMS Magnetic–Angular Rate–Gravity (MARG) Sensor Manipulated in Regions with and without Geomagnetic Distortion |
| title_full_unstemmed | Benchmarking Dataset of Signals from a Commercial MEMS Magnetic–Angular Rate–Gravity (MARG) Sensor Manipulated in Regions with and without Geomagnetic Distortion |
| title_short | Benchmarking Dataset of Signals from a Commercial MEMS Magnetic–Angular Rate–Gravity (MARG) Sensor Manipulated in Regions with and without Geomagnetic Distortion |
| title_sort | benchmarking dataset of signals from a commercial mems magnetic angular rate gravity marg sensor manipulated in regions with and without geomagnetic distortion |
| topic | MARG MIMU orientation estimation sensor fusion algorithm dataset orientation algorithm benchmarking |
| url | https://www.mdpi.com/1424-8220/23/8/3786 |
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