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Design and Development of Human Skill Transfer System with Augmented Reality

Tarinee Tonggoed and Siam Charoenseang
Institute of Field Robotics, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

Abstract—This paper presents the design and development of a skill transferring system of a human’s hand trajectory. Aims of this system are capturing, encoding, and playing back of the demonstrator’s hand trajectory in both spatial and temporal data. To capture a human’s hand movement, the Microsoft’s Kinect sensor is selected to track the human skeleton and send it to the trajectory modeler to encode the demonstrator’s movement. The Gaussian mixture model with its modification of initial parameter estimation is applied in the encoding process. In the playback phase, augmented reality is implemented to provide the learner with some guided positions and times in the form of 2D and 3D computer graphics. The result of applying the proposed algorithm in Gaussian mixture model with initial parameter estimation which is compared with the traditional k-means and incremental k-means approaches takes the least training time. In the playback process, the averaged errors of the playback graphics information for the hand movement skill transfer system are about 2.7 cm. in x axis and 6.25 cm. in y axis. The implementation results demonstrate that the proposed system can capture, encode, and play back the demonstrator’s hand trajectory along with augmented reality effectively.

Index Terms—human skill transfer, motor learning, augmented reality

Cite: Tarinee Tonggoed and Siam Charoenseang, "Design and Development of Human Skill Transfer System with Augmented Reality," Jounal of Automation and Control Engineering, Vol. 3, No. 5, pp. 403-409, October, 2015. doi: 10.12720/joace.3.5.403-409

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