Adaptive Kinematic Modeling for Improved Hand Posture Estimates Using a Haptic Glove

TL;DR

This paper enhances the accuracy of hand-posture measurements in haptic gloves by adding sensors and applying kinematic models, aiming for precise digital hand recreation in biomedical applications like VR therapy.

Contribution

It introduces sensor augmentations and a kinematic modeling approach to improve hand-posture measurement accuracy in commercial haptic gloves.

Findings

Enhanced measurement accuracy demonstrated through evaluation

Kinematic modeling effectively integrates additional sensors

Potential for improved digital hand recreation in biomedical settings

Abstract

Most commercially available haptic gloves compromise the accuracy of hand-posture measurements in favor of a simpler design with fewer sensors. While inaccurate posture data is often sufficient for the task at hand in biomedical settings such as VR-therapy-aided rehabilitation, measurements should be as precise as possible to digitally recreate hand postures as accurately as possible. With these applications in mind, we have added extra sensors to the commercially available Dexmo haptic glove by Dexta Robotics and applied kinematic models of the haptic glove and the user's hand to improve the accuracy of hand-posture measurements. In this work, we describe the augmentations and the kinematic modeling approach. Additionally, we present and discuss an evaluation of hand posture measurements as a proof of concept.