A novel data glove using inertial and magnetic sensors for motion capture and robotic arm-hand teleoperation

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Abstract
Purpose: The purpose of this paper is to present a novel data glove which can capture the motion of the arm and hand by inertial and magnetic sensors. The proposed data glove is used to provide the information of the gestures and teleoperate the robotic arm-hand. Design/methodology/approach: The data glove comprises 18 low-cost inertial and magnetic measurement units (IMMUs) which not only make up the drawbacks of traditional data glove that only captures the incomplete gesture information but also provide a novel scheme of the robotic arm-hand teleoperation. The IMMUs are compact and small enough to wear on the upper arm, forearm, palm and fingers. The calibration method is proposed to improve the accuracy of measurements of units, and the orientations of each IMMU are estimated by a two-step optimal filter. The kinematic models of the arm, hand and fingers are integrated into the entire system to capture the motion gesture. A positon algorithm is also deduced to compute the positions of fingertips. With the proposed data glove, the robotic arm-hand can be teleoperated by the human arm, palm and fingers, thus establishing a novel robotic arm-hand teleoperation scheme. Findings: Experimental results show that the proposed data glove can accurately and fully capture the fine gesture. Using the proposed data glove as the multiple input device has also proved to be a suitable teleoperating robotic arm-hand system. Originality/value: Integrated with 18 low-cost and miniature IMMUs, the proposed data glove can give more information of the gesture than existing devices. Meanwhile, the proposed algorithms for motion capture determine the superior results. Furthermore, the accurately captured gestures can efficiently facilitate a novel teleoperation scheme to teleoperate the robotic arm-hand.

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