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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.

While the availability of low-cost micro electro-mechanical systems (MEMS) accelerometers, gyroscopes, and magnetometers initially seemed to promise the possibility of using them to easily track the position and orientation of virtually any object that they could be attached to, this promise has not yet been fulfilled. Navigation-grade accelerometers and gyroscopes have long been the basis for tracking ships and aircraft, but the signals from low-cost MEMS accelerometers and gyroscopes are still orders of magnitude poorer in quality (e.g., bias stability). Therefore, the applications of MEMS inertial measurement units (IMUs), containing tri-axial accelerometers and gyroscopes, are currently not as extensive as they were expected to be. Even the addition of MEMS tri-axial magnetometers, to conform magnetic, angular rate, and gravity (MARG) sensor modules, has not fully overcome the challenges involved in using these modules for long-term orientation estimation, which would be of great benefit for the tracking of human–computer hand-held controllers or tracking of Internet-Of-Things (IoT) devices. Here, we present an algorithm, GMVDμK (or simply GMVDK), that aims at taking full advantage of all the signals available from a MARG module to robustly estimate its orientation, while preventing damaging overcorrections, within the context of a human–computer interaction application. Through experimental comparison, we show that GMVDK is more robust to magnetic disturbances than three other MARG orientation estimation algorithms in representative trials.

This paper presents the theoretical foundation, practical implementation, and empirical evaluation of a glove for interaction with 3-D virtual environments. At the dawn of the “Spatial Computing Era”, where users continuously interact with 3-D Virtual and Augmented Reality environments, the need for a practical and intuitive interaction system that can efficiently engage 3-D elements is becoming pressing. Over the last few decades, there have been attempts to provide such an interaction mechanism using a glove. However, glove systems are currently not in widespread use due to their high cost and, we propose, due to their inability to sustain high levels of performance under certain situations. Performance deterioration has been observed due to the distortion of the local magnetic field caused by ordinary ferromagnetic objects present near the glove’s operating space. There are several areas where reliable hand-tracking gloves could provide a next generation of improved solutions, such as American Sign Language training and automatic translation to text and training and evaluation for activities that require high motor skills in the hands (e.g., playing some musical instruments, training of surgeons, etc.). While the use of a hand-tracking glove toward these goals seems intuitive, some of the currently available glove systems may not meet the accuracy and reliability levels required for those use cases. This paper describes our concept of an interaction glove instrumented with miniature magnetic, angular rate, and gravity (MARG) sensors and aided by a single camera. The camera used is an off-the-shelf red, green, and blue–depth (RGB-D) camera. We describe a proof-of-concept implementation of the system using our custom “GMVDK” orientation estimation algorithm. This paper also describes the glove’s empirical evaluation with human-subject performance tests. The results show that the prototype glove, using the GMVDK algorithm, is able to operate without performance losses, even in magnetically distorted environments.

This dissertation pursued the definition and evaluation of a processing approach for robust real-time orientation estimation of a miniature Magnetic, Angular-Rate, Gravity (MARG) module for use in a human-computer interaction system that also uses a 3-camera IR-video module for position estimation. The proposed algorithm introduces the novel idea of spatially mapping the level of trustworthiness of the magnetometer-based potential corrections to the orientation estimate. This trustworthiness level is used to reduce the strength of magnetometer-based corrections of the orientation estimate where the magnetic field distortion invalidates the assumptions necessary for those corrections. The new algorithm addresses the three research questions posed in this dissertation by 1.) Compensating for the gyroscope drift error 2.) Creating a voxel map with the values of magnetic distortion in specific regions of the operating space of the system, and 3.) Combining two different types of data to accurately track hand motion through adaptive quaternion interpolation. The algorithm was evaluated in an experiment with thirty human subjects, processing signals from one MARG module and a 3-camera IR video system. The results verified that the new algorithm, using the Gravity Vector and the Magnetic North vector with Double SLERP interpolation (GMV-D), reduced the drift of the orientation estimates in areas with and without magnetic distortion. The Kruskal-Wallis test, with the error in the Phi, Theta, and Psi Euler angles as dependent variables, was used to study 3 orientation estimation methods: Kalman Filtering, GMV-D, and its precursor, GMV-S (which uses a single SLERP operation). In the magnetically undistorted area, there were no significant differences for the Phi and Theta angles. However, in the magnetically distorted area, significant differences in method performance were found for all three Euler angles, with GMV-D consistently reporting the lowest mean rank and the Kalman Filter reporting the highest. The proposed GMV-D method makes the MARG orientation estimation more robust by fully taking advantage of the MARG operating conditions in a typical human-computer interaction application and by comprehensively utilizing all the sensing modalities available in the MARG module.