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Multi-recursive Wavelet Neural Network for Proximity Capacitive Gesture Recognition Analysis and Implementation

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New Trends in Computer Technologies and Applications (ICS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1013))

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Abstract

This paper presents a multi-recursive wavelet neural network (MRWNN) with proximity capacitive gesture recognition. Recently, the capacitive sensor technologies have been developed for proximity methods that sensing electronic varies around sensor detection point, but the user gesture signals are time-variant. The MRWNN have multi layers recursive weight to record last signal variation, and we utilize microcontroller with MRWNN to identify algorithms and implement proximity capacitive gesture recognition. Moreover, we show MRWNN weight convergence analysis of the MRWNN signal identifier. In the experimental results, we show MRWNN can recognize patterns of different gesture signal accurately and reliably. In addition, we use wearable device combined with BLE (Bluetooth Low Energy) feedback output response immediately.

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References

  1. Lin, X., Adhikary, A., Wang, Y.P.E.: Random access preamble design and detection for 3GPP narrowband IoT systems. IEEE Wirel. Commun. Lett. 5(6), 640–643 (2016)

    Article  Google Scholar 

  2. Chu, C.T., Chiang, H.K., Hung, J.J.: Dynamic heart rate monitors algorithm for reflection green light wearable device. In: International Conference on Intelligent Informatics and Biomedical Sciences, vol. 62, no. 8, pp. 438–445 (2015)

    Google Scholar 

  3. Ha, U., Lee, Y., Kim, H.T., Roh, J., Kim, B.C., Yoo, H.J.: A Wearable EEG-HEG-HRV multimodal system with simultaneous monitoring of tES for mental health management. IEEE Trans. Biomed. Circuits Syst. 9(6), 758–766 (2015)

    Google Scholar 

  4. Brugarolas, R., et al.: Wearable heart rate sensor systems for wireless canine health monitoring. IEEE Sens. J. 16(10), 3454–3464 (2016)

    Article  Google Scholar 

  5. Wiens, A.D., Etemadi, M., Roy, S., Klein, L., Inan, O.T.: Toward continuous, noninvasive assessment of ventricular function and hemodynamics: wearable ballistocardiography. IEEE J. Biomed. Health Inform. 22(1), 1435–1442 (2015)

    Article  Google Scholar 

  6. Semnani, S.H., Basir, O.A.: Semi-flocking algorithm for motion control of mobile sensors in large-scale surveillance systems. IEEE Trans. Cybern. 45(1), 129–137 (2015)

    Article  Google Scholar 

  7. Fazio, M., Puliafito, A.: Cloud4sens: a cloud-based architecture for sensor controlling and monitoring. IEEE Commun. Mag. 41(47), 4–8 (2015)

    Google Scholar 

  8. Kafi, M.A., Djenouri, D., Othman, J.B., Badache, N.: Congestion control protocols in wireless sensor networks: a survey, and future trends. IEEE Commun. Surv. Tutor. 16(3), 1369–1390 (2014)

    Article  Google Scholar 

  9. Giles, C.L., Miller, C.B., Chen, D., Chen, H.H., Sun, G.Z., Lee, Y.C.: Learning and extracting finite state automata with second-order recurrent neural networks. Neural Comput. 4(3), 518–527 (1992)

    Article  Google Scholar 

  10. Kim, Y., Moon, T.: Human detection and activity classification based on micro-doppler signatures using deep convolutional neural networks. IEEE Trans. Ind. Electron. 13(1), 8–12 (2016)

    Google Scholar 

  11. Bu, N., Okamoto, M., Tsuji, T.: A hybrid motion classification approach for EMG-based human–robot interfaces using bayesian and neural networks. IEEE Trans. Rob. 25(3), 502–511 (2009)

    Article  Google Scholar 

  12. Gupta, H.P., Chudgar, H.S., Mukherjee, S., Dutta, T.: A continuous hand gestures recognition technique for human-machine interaction using accelerometer and gyroscope sensors. IEEE Sens. J. 16(16), 6425–6432 (2016)

    Article  Google Scholar 

  13. Chang, J.Y.: Nonparametric feature matching based conditional random fields for gesture recognition from multi-modal video. IEEE Trans. Pattern Anal. Mach. Intell. 38(8), 1612–1625 (2016)

    Article  Google Scholar 

  14. Lefter, I., Burghouts, G.J., Rothkrantz, L.J.M.: Recognizing stress using semantics and modulation of speech and gestures. IEEE Trans. Affect. Comput. 38(8), 162–175 (2017)

    Google Scholar 

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Acknowledgment

This work was supported in part by the “Intelligent Recognition Industry Service Center” of Higher Education Sprout Project, Ministry of Education, Taiwan.

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Authors and Affiliations

  1. Internet of Things Laboratory, Chunghwa Telecom Laboratories, New Taipei City, Taiwan

    Chao-Ting Chu

  2. Department of Electrical Engineering, National Yunlin University of Science and Technology, Douliu, Taiwan

    Chian-Cheng Ho

Authors
  1. Chao-Ting Chu
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  2. Chian-Cheng Ho
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Corresponding author

Correspondence to Chao-Ting Chu .

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Editors and Affiliations

  1. National Yunlin University of Science and Technology, Douliu, Taiwan

    Chuan-Yu Chang

  2. National Yunlin University of Science and Technology, Douliu, Taiwan

    Chien-Chou Lin

  3. Southern Taiwan University of Science and Technology, Tainan, Taiwan

    Horng-Horng Lin

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Chu, CT., Ho, CC. (2019). Multi-recursive Wavelet Neural Network for Proximity Capacitive Gesture Recognition Analysis and Implementation. In: Chang, CY., Lin, CC., Lin, HH. (eds) New Trends in Computer Technologies and Applications. ICS 2018. Communications in Computer and Information Science, vol 1013. Springer, Singapore. https://doi.org/10.1007/978-981-13-9190-3_53

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  • DOI: https://doi.org/10.1007/978-981-13-9190-3_53

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9189-7

  • Online ISBN: 978-981-13-9190-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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