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.
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This content will become publicly available on July 9, 2024
A Self-contained Approach to MEMS MARG Orientation Estimation for Hand Gesture Tracking in Magnetically Distorted Environments
There is increasing interest in using low-cost and lightweight Micro
Electro-Mechanical System (MEMS) modules containing tri-axial accelerometers,
gyroscopes and magnetometers for tracking the motion of segments of the
human body. We are specifically interested in using these devices, called “Magnetic,
Angular-Rate and Gravity” (“MARG”) modules, to develop an instrumented
glove, assigning one of these MARG modules to monitor the (absolute) 3-D orientation
of each of the proximal and middle phalanges of the fingers of a computer
user. This would provide real-time monitoring of the hand gestures of the user,
enabling non-vision gesture recognition approaches that do not degrade with lineof-
sight disruptions or longer distance from the cameras. However, orientation
estimation from low-cost MEMS MARG modules has shown to degrade in areas
where the geomagnetic field is distorted by the presence of ferromagnetic objects
(which are common in contemporary environments). This paper describes the continued
evolution of our algorithm to obtain robust MARG orientation estimates,
even in magnetically distorted environments. In particular, the paper describes a
new self-contained version of the algorithm, i.e., one requiring no information
from external devices, in contrast to the previous versions.
Keywords: MARG module · Orientation Estimation · Magnetic Disturbance
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- NSF-PAR ID:
- 10458503
- Editor(s):
- M. Kurosu and A. Hashizume
- Date Published:
- Journal Name:
- HCII 2023, LNCS 14011
- Page Range / eLocation ID:
- 585–602
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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