Abstract
The recognition of grasp type is essential for a more detailed analysis of human action. In this paper, we propose a novel method for real-time grasp type recognition using Leap motion controller (LMC). Our proposal is based on the tracking data provided by the LMC sensor and a series of feature descriptors are introduced and extracted from LMC data. Combining the feature descriptors of relative positions of thumb, finger joint angles and finger directions lead to the best representation of the arrangement of the fingers. And then the grasp type classification can be achieved by using a SVM classifier. An experimental study of our approach is addressed and we show that recognition rate could be improved. The current implementation is also can satisfy the real-time requirements.
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Acknowledgements
The authors would like to acknowledge the support from the Natural Science Foundation of China under Grant No. 51405481 and the Natural Science Foundation of Liaoning Province under Grant No. 20180551124.
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Zou, Y., Liu, H., Zhang, J. (2019). Real-Time Grasp Type Recognition Using Leap Motion Controller. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11742. Springer, Cham. https://doi.org/10.1007/978-3-030-27535-8_42
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DOI: https://doi.org/10.1007/978-3-030-27535-8_42
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