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      Real-time 3D gesture visualisation for the study of Sign Language

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      Electronic Visualisation and the Arts (EVA 2012) (EVA)

      Electronic Visualisation and the Arts

      10 - 12 July 2012

      Gesture visualisation, Sign Language, Motion capture, Writing, Graphical system, Iconicization

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          This work constitutes a contribution to the emergence of a common writing for French Sign Language in a graphical or even a typographical framework. In this article we present ThirdEye, an interactive visualisation tool designed for movement analysis. As a research tool, ThirdEye’s main objective is to help study the importance of movement in the production of meaning within the context of sign language. In this paper, we will show why and how this device was conceived and how it can help to shed light on the relationships between sign language and writing.

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          Most cited references 3

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          A Survey of Computer Vision-Based Human Motion Capture

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            Estimation of IMU and MARG orientation using a gradient descent algorithm.

            This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications. It is applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity (MARG) sensor arrays that also include tri-axis magnetometers. The MARG implementation incorporates magnetic distortion compensation. The algorithm uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimised gradient descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. Performance has been evaluated empirically using a commercially available orientation sensor and reference measurements of orientation obtained using an optical measurement system. Performance was also benchmarked against the propriety Kalman-based algorithm of orientation sensor. Results indicate the algorithm achieves levels of accuracy matching that of the Kalman based algorithm; < 0.8° static RMS error, < 1.7° dynamic RMS error. The implications of the low computational load and ability to operate at small sampling rates significantly reduces the hardware and power necessary for wearable inertial movement tracking, enabling the creation of lightweight, inexpensive systems capable of functioning for extended periods of time. © 2011 IEEE
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              Game-Console Makers Battle over Motion-Sensitive Controllers


                Author and article information

                July 2012
                July 2012
                : 275-280
                GestuelScript (ESAD Amiens)
                UMR 7023 SFL, CNRS,

                Université Paris 8 et Université EVE
                © Roman Miletitch et al. Published by BCS Learning and Development Ltd. Electronic Visualisation and the Arts (EVA 2012), London, UK

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit

                Electronic Visualisation and the Arts (EVA 2012)
                London, UK
                10 - 12 July 2012
                Electronic Workshops in Computing (eWiC)
                Electronic Visualisation and the Arts
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page):
                Electronic Workshops in Computing


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