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Volume 9 Issue 3
Mar.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2022  >  9(3): 510-519
B. X. Wu, J. P. Zhong, and C. G. Yang, “A visual-based gesture prediction framework applied in social robots,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 510–519, Mar. 2022. doi: 10.1109/JAS.2021.1004243
Citation: B. X. Wu, J. P. Zhong, and C. G. Yang, “A visual-based gesture prediction framework applied in social robots,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 510–519, Mar. 2022. doi: 10.1109/JAS.2021.1004243
shu

A Visual-Based Gesture Prediction Framework Applied in Social Robots

doi: 10.1109/JAS.2021.1004243
  • 1.

    College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China

  • 2.

    Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 511442, China

Funds:  This work was supported in part by National Nature Science Foundation of China (NSFC) (U20A20200, 61861136009), in part by Guangdong Basic and Applied Basic Research Foundation (2019B1515120076, 2020B1515120054), in part by Industrial Key Technologies R & D Program of Foshan (2020001006308)
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    Abstract
  • In daily life, people use their hands in various ways for most daily activities. There are many applications based on the position, direction, and joints of the hand, including gesture recognition, gesture prediction, robotics and so on. This paper proposes a gesture prediction system that uses hand joint coordinate features collected by the Leap Motion to predict dynamic hand gestures. The model is applied to the NAO robot to verify the effectiveness of the proposed method. First of all, in order to reduce jitter or jump generated in the process of data acquisition by the Leap Motion, the Kalman filter is applied to the original data. Then some new feature descriptors are introduced. The length feature, angle feature and angular velocity feature are extracted from the filtered data. These features are fed into the long-short time memory recurrent neural network (LSTM-RNN) with different combinations. Experimental results show that the combination of coordinate, length and angle features achieves the highest accuracy of 99.31%, and it can also run in real time. Finally, the trained model is applied to the NAO robot to play the finger-guessing game. Based on the predicted gesture, the NAO robot can respond in advance.

     

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    Highlights

    • A method for predicting gestures based on the LSTM is proposed. The data of gestures is collected by the Leap Motion
    • In order to reduce or eliminate the jitter or jump generated in the process of acquiring data by the Leap Motion, the Kalman filter is applied to solve this problem effectively
    • We propose a reliable feature extraction method, which extracts coordinate features, length features, angle features and angular velocity features, and combines these features to predict gestures
    • We apply the trained model to the NAO robot and make it play the finger-guessing game with players, which effectively verifies the real-time and accuracy of the proposed approach

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