通过加装传感器与运动学建模，提升触觉手套的手部姿态估计精度。
Adaptive Kinematic Modeling for Improved Hand Posture Estimates Using a Haptic Glove
- 在Dexmo手套上增补传感器并构建用户手部与手套的运动学模型。
- 实验验证了改进后手部姿态估计的准确率显著提升。
- 适合需要高精度手部动作捕捉的康复训练与虚拟现实应用。
市面上大多数触觉手套为简化设计而牺牲了手部姿态测量的精度。尽管在如虚拟现实辅助康复等生物医学场景中，较低精度的姿态数据可能已足够，但若要精确数字重建手部姿态，仍需尽可能提高测量精度。为此，本文在Dexta Robotics公司生产的Dexmo触觉手套基础上增加了额外传感器，并结合手套与用户手部的运动学模型，以提升手部姿态估计的准确性。本文详细描述了硬件改造与运动学建模方法，并展示了手部姿态测量的评估结果，作为概念验证。
原文摘要 · Abstract (English)
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.
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