Abstract
Dynamic hand gesture recognition plays an important role in human–computer Interaction. This paper proposes a novel method for dynamic hand gesture recognition using wireless signals. Through the analysis of wireless frame structure, the preamble’s signal of 802.11a is collected through Software Defined Radio platform and reserved as the data source. In addition, more than one time-domain feature sequences perform unique shape for different dynamic hand gesture. These sequences are split into single cycle (time-series) and the unavoidable electronic interference is reduced through discrete wavelet transform. At the same time, due to fuzziness of dynamic hand gesture, the amplitude and duration for the same dynamic hand gesture are not exactly same. Therefore, the parallel HMM models which represent for different hand gestures and features are built for recognition. The result shows that the average recognition rate is about 90.5% for dynamic hand gesture recognition.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (61671075), National Natural Science Foundation of China (61631003), and National Natural Science Foundation of China (61171176).
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Xu, J., Jiang, T. (2018). Dynamic Hand Gesture Recognition Based on Parallel HMM Using Wireless Signals. In: Liang, Q., Mu, J., Wang, W., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2016. Lecture Notes in Electrical Engineering, vol 423. Springer, Singapore. https://doi.org/10.1007/978-981-10-3229-5_80
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DOI: https://doi.org/10.1007/978-981-10-3229-5_80
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