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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. 

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. 

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.