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Article citations


Vantigodi, S. and Radhakrishnan, V.B. (2014) Action Recognition from Motion Capture Data Using Meta-Cognitive RBF Network Classifier. Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2014 IEEE Ninth International Conference, Singapore, 21-24 April 2014, 1-6. https://doi.org/10.1109/ISSNIP.2014.6827664

has been cited by the following article:

  • TITLE: Kinect-Based Motion Recognition Tracking Robotic Arm Platform

    AUTHORS: Jinxiao Gao, Yinan Chen, Fuhao Li

    KEYWORDS: Kinect, Arduino, Bone Angle, Motion Tracking, Robotic Arm Platform

    JOURNAL NAME: Intelligent Control and Automation, Vol.10 No.3, May 29, 2019

    ABSTRACT: The development of artificial intelligence technology has promoted the rapid improvement of human-computer interaction. This system uses the Kinect visual image sensor to identify human bone data and complete the recognition of the operator’s movements. Through the filtering process of real-time data by the host computer platform with computer software as the core, the algorithm is programmed to realize the conversion from data to control signals. The system transmits the signal to the lower computer platform with Arduino as the core through the transmission mode of the serial communication, thereby completing the control of the steering gear. In order to verify the feasibility of the theory, the team built a 4-DOF robotic arm control system and completed software development. It can display other functions such as the current bone angle and motion status in real time on the computer operation interface. The experimental data shows that the Kinect-based motion recognition method can effectively complete the tracking of the expected motion and complete the grasping and transfer of the specified objects, which has extremely high operability.