An official website of the United States government
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.
more »
« less
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
more »
« less
- 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
More Like this
-
-
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.more » « less
-
Masaaki Kurosu (Ed.)A new approach to correct the orientation estimate for a miniature Magnetic-Angular Rate-Gravity (MARG) module is statistically evaluated in a hand motion tracking system. Thirty human subjects performed an experiment to validate the performance of the proposed orientation correction algorithm in both non-magnetically distorted (MN) and magnetically distorted (MD) areas. The Kruskal-Wallis tests show that the orientation correction algorithm using Gravity and Magnetic Vectors with Double SLERP (GMV-D), the correction using Gravity and Magnetic Vectors with Single SLERP (GMV-S) and the on-board Kalman-Filter (KF) performed similarly in non-magnetically distorted areas. However, the statistical tests show that, when operating in the magnetically distorted region, the level of error in the orientation estimates produced by the three methods is significantly different, with the proposed GMV-D method yielding lower levels of error in the three Euler Angles Phi, Theta and Psi. This indicates that the GMV-D method was better able to provide orientation estimates that are more robust against local disturbances of the magnetic field that might exist in the operating space of the MARG module.more » « less
-
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.more » « less
-
Numerous applications of Virtual Reality (VR) and Augmented Reality (AR) continue to emerge. However, many of the current mechanisms to provide input in those environments still require the user to perform actions (e.g., press a number of buttons, tilt a stick) that are not natural or intuitive. It would be desirable to enable users of 3D virtual environments to use natural hand gestures to interact with the environments. The implementation of a glove capable of tracking the movement and configuration of a user’s hand has been pursued by multiple groups in the past. One of the most recent approaches consists of tracking the motion of the hand and fingers using miniature sensor modules with magnetic and inertial sensors. Unfortunately, the limited quality of the signals from those sensors and the frequent deviation from the assumptions made in the design of their operations have prevented the implementation of a tracking glove able to achieve high performance and large-scale acceptance. This paper describes our development of a proof-of-concept glove that incorporates motion sensors and a signal processing algorithm designed to maintain high tracking performance even in locations that are challenging to these sensors, (e.g., where the geomagnetic field is distorted by nearby ferromagnetic objects). We describe the integration of the required components, the rationale and outline of the tracking algorithms and the virtual reality environment in which the tracking results drive the movements of the model of a hand. We also describe the protocol that will be used to evaluate the performance of the glove.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
