V. Lantz

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Publications (2)2.12 Total impact

  • Article: A Framework for Hand Gesture Recognition Based on Accelerometer and EMG Sensors
    Xu Zhang, Xiang Chen, Yun Li, V. Lantz, Kongqiao Wang, Jihai Yang
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    ABSTRACT: This paper presents a framework for hand gesture recognition based on the information fusion of a three-axis accelerometer (ACC) and multichannel electromyography (EMG) sensors. In our framework, the start and end points of meaningful gesture segments are detected automatically by the intensity of the EMG signals. A decision tree and multistream hidden Markov models are utilized as decision-level fusion to get the final results. For sign language recognition (SLR), experimental results on the classification of 72 Chinese Sign Language (CSL) words demonstrate the complementary functionality of the ACC and EMG sensors and the effectiveness of our framework. Additionally, the recognition of 40 CSL sentences is implemented to evaluate our framework for continuous SLR. For gesture-based control, a real-time interactive system is built as a virtual Rubik's cube game using 18 kinds of hand gestures as control commands. While ten subjects play the game, the performance is also examined in user-specific and user-independent classification. Our proposed framework facilitates intelligent and natural control in gesture-based interaction.
    IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans 12/2011; · 2.12 Impact Factor
  • Conference Proceeding: Hand Gesture Recognition Research Based on Surface EMG Sensors and 2D-accelerometers
    Xiang Chen, Xu Zhang, Zhang-Yan Zhao, Ji-Hai Yang, V. Lantz, Kong-Qiao Wang
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    ABSTRACT: For realizing multi-DOF interfaces in wearable computer system, accelerometers and surface EMG sensors are used synchronously to detect hand movement information for multiple hand gesture recognition. Experiments were designed to collect gesture data with both sensing techniques to compare their performance in the recognition of various wrist and finger gestures. Recognition tests were run using different subsets of information: accelerometer and sEMG data separately and combined sensor data. Experimental results show that the combination of sEMG sensors and accelerometers achieved 5-10% improvement in the recognition accuracies for hand gestures when compared to that obtained using sEMG sensors solely.
    Wearable Computers, 2007 11th IEEE International Symposium on; 11/2007
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