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Non-audio–Video Gesture Recognition Systems

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Abstract

Gesture recognition as a topic in computer science and language technology has the goal of interpreting human gestures via mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Gesture recognition enables humans to communicate with the machine and interact naturally without any mechanical devices. This paper investigates the possibility to use non-audio/video sensors to design a low-cost gesture recognition device that can be connected to any computer on the market. The paper proposes an equation that relates the distance and voltage for a Sharp GP2Y0A21 and GP2D120 sensors in the situation that a hand is used as the reflective object. In the end, the presented system is compared with other audio/video system that exist on the market. Also, future research is shown of a glove-like device for sign-language translation.

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References

  1. Fan, W., Chen, X., Wang, W., Zhang, X., Yang J., Lantz, V., et al. (2010). A method of hand gesture recognition based on multiple sensors. In 2010 4th international conference on bioinformatics and biomedical engineering (iCBBE) (pp. 1–4), June 18–20, 2010.

  2. Ren, Z., Yuan, J., Meng, J., & Zhang, Z. (2013). Robust part-based hand gesture recognition using kinect sensor. IEEE Transactions on Multimedia,15(5), 1110–1120.

    Article  Google Scholar 

  3. Wang, Y., Yang, C., Wu, X., Xu, S., & Li, H. (2012). Kinect based dynamic hand gesture recognition algorithm research. In 2012 4th International Conference on intelligent human-machine systems and cybernetics (IHMSC) (Vol. 1, pp. 274–279), August 26–27, 2012.

  4. Erden, F., Bingol, A. S., & Cetin, A. E. (2014). Hand gesture recognition using two differential PIR sensors and a camera. In Signal processing and communications applications conference (SIU), 2014 22nd (pp. 349–352), April 23–25, 2014.

  5. Arduino Leonardo specifications page. (2016). Available: http://arduino.cc/en/Main/arduinoBoardLeonardo.

  6. Datasheet for Parallax PING))). (2016). Available: http://www.parallax.com/sites/default/files/downloads/28015-PING-Sensor-Product-Guidev2.0.pdf.

  7. Datasheet for PIR SE-10 sensor. (2016). Available: http://www.pololu.com/file/0J250/SE-10.pdf.

  8. Datasheet for Sharp IR GP2D120. (2016). Available: http://www.sharpsma.com/webfm_send/1205.

  9. Datasheet for Sharp IR GP2Y0A21. (2016). Available: http://www.sharpsma.com/webfm_send/1489.

  10. Datasheet for Arduino TFT LCD Screen. (2016). Available: http://arduino.cc/en/uploads/Main/HTF0177SN-01-SPEC.pdf.

  11. Kyriazakos, S., Mihaylov, M., Anggorojati, B., Mihovska, A., Craciunescu, R., Fratu, O., et al. (2015). eWALL—An intelligent caring home environment offering personalized context-aware applications based on advanced sensing. Wireless Personal Communications Journal,87, 1–19.

    Google Scholar 

  12. Craciunescu, R., Halunga, S., & Fratu, O. (2015). Wireless ZigBee home automation system. In Proceedings of SPIE 9258, advanced topics in optoelectronics, microelectronics, and nanotechnologies VII, 925826, February 21, 2015.

  13. Microsoft Kinect Sensor. (2016). https://www.microsoft.com/en-us/kinectforwindows/purchase/sensor_setup.aspx.

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Acknowledgements

This work has been funded by European Social Fund, the Human Capital operational programme Priority Axis 6- European and competencies, through the project “Developing the entrepreneurial skills of doctoral and postdoctoral students - key to career success (A-Succes)” Contract no. 51675/09.07.2019 POCU/380/6/13 - SMIS code: 125125); European Commission by FP7 IP Project No. 610658/2013 “eWALL for Active Long Living-eWALL” by the Sectoral Operational Programme Human Resources Development 2007–2013 of the Ministry of European Funds through the Financial Agreement POSDRU/159/1.5/S/132397 and by the ERDF funded project “Research Ecosystems for development and innovation of IT&C services and products for a society connected to IoT—NETIO”.

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  1. University Politechnica of Bucharest, Bucharest, Romania

    Razvan Craciunescu

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  1. Razvan Craciunescu
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Correspondence to Razvan Craciunescu.

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Craciunescu, R. Non-audio–Video Gesture Recognition Systems. Wireless Pers Commun 110, 815–827 (2020). https://doi.org/10.1007/s11277-019-06757-5

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