Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Research on User Experience Quality Evaluation Method of Internet of Vehicles Based on sEMG Signal

  • Conference paper
  • First Online:
Simulation Tools and Techniques (SIMUtools 2020)

Included in the following conference series:

  • 929 Accesses

Abstract

In recent years, with the rapid development of Internet of vehicles, service providers and operators need to constantly upgrade and optimize their related services (communication nodes, terminal driving safety monitoring, intelligent vehicle), and the evaluation of end-user experience quality is the core of improving business. From the above point of view, this paper proposes an effective method to evaluate the psychological perception of end users by using the SEMG (Surface electromyograph) of end users. The method uses time domain features to represent the changing trend of users in different emotional states, and maps the relationship between psychological perception, so as to effectively evaluate the experience quality of end users. The results show that the method can adapt to the evaluation of experience quality of end users in the Internet of vehicles, obtain the psychological experience quality of current users, and provide strong support for service providers and operators to improve their core business of vehicle network.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Jiang, D., Wang, Y., Lv, Z., et al.: Big data analysis-based network behavior insight of cellular networks for industry 4.0 applications. IEEE Trans. Ind. Inf. 16(2), 1310–1320 (2020)

    Article  Google Scholar 

  2. Xiong, A.B., Ding, Q.C., Zhao, X.G., Han, J.D., Liu, G.J.: Classification of hand gestures based on single-channel sEMG decomposition. Jixie Gongcheng Xuebao/J. Mech. Eng. 52(7), 6–13 (2016)

    Article  Google Scholar 

  3. Jiang, D., Wang, Y., Lv, Z., et al.: Intelligent optimization-based reliable energy-efficient networking in cloud services for IIoT networks. IEEE J. Selected Areas Commun. (2019)

    Google Scholar 

  4. Li, Y., Tian, Y.T., Xu, Z.J., Yang, Z.M.: Multi-channel sEMG detection and pattern recognition. In: Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014, pp. 845–850,(2014)

    Google Scholar 

  5. Jiang, D., Wang, Y., Lv, Z., et al.: A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Trans. Intell. Transp. Syst. 19(10), 3305–3319 (2018)

    Article  Google Scholar 

  6. Herrera, E.V., et al.: SEMG onset detection caused by temperature variation. In: 2019 6th International Conference on Control, Decision and Information Technologies, CoDIT, pp. 1539–1543 (2019)

    Google Scholar 

  7. Jiang, D., Huo, L., Song, H.: Rethinking behaviors and activities of base stations in mobile cellular networks based on big data analysis. IEEE Trans. Netw. Sci. Eng. 1(1), 1–2 (2018)

    MathSciNet  Google Scholar 

  8. Jiang, D., Wang, W., Shi, L., et al.: A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Trans. Netw. Sci. Eng. 5(3), 1–12 (2018)

    Google Scholar 

  9. de Melo, W.C., de Lima Filho, E.B., da Silva Júnior, W.S.: SEMG signal compression based on two-dimensional techniques. BioMed. Eng. Online 15(1), 1–31 (2016)

    Article  Google Scholar 

  10. Jiang, D., Huo, L., Li, Y.: Fine-granularity inference and estimations to network traffic for SDN. PLoS ONE 13(5), 1–23 (2018)

    Google Scholar 

  11. Li, L., Wang, J.H.: An improved method for sEMG segmentation. In: Proceedings of the 2012 24th Chinese Control and Decision Conference, CCDC 2012, pp. 1308–1311 (2012)

    Google Scholar 

  12. Jiang, D., Zhang, P., Lv, Z., et al.: Energy-efficient multi-constraint routing algorithm with load balancing for smart city applications. IEEE Internet Things J. 3(6), 1437–1447 (2016)

    Article  Google Scholar 

  13. Kushwah, K., Narvey, R., Singhal, A.: Head posture analysis using sEMG signal. In: 2018 International Conference on Advanced Computation and Telecommunication, ICACAT (2018)

    Google Scholar 

  14. Jiang, D., Li, W., Lv, H.: An energy-efficient cooperative multicast routing in multi-hop wireless networks for smart medical applications. Neurocomputing 220(2017), 160–169 (2017)

    Article  Google Scholar 

  15. Wang, Y., Jiang, D., Huo, L., et al.: A new traffic prediction algorithm to software defined networking. Mob. Netw. Appl. (2019)

    Google Scholar 

  16. Wang, X., Zhang, Y., Zhao, D.C., Wan, X.P., Peng, C.L.: Design and experimental study of wireless multichannel sEMG acquisition system. Yi Qi Yi Biao Xue Bao/Chin. J. Sci. Instrum. 33(11), 2460–2465 (2012)

    Google Scholar 

  17. Qi, S., Jiang, D., Huo, L.: A prediction approach to end-to-end traffic in space information networks. Mob. Netw. Appl. (2019)

    Google Scholar 

  18. Wang, F., Jiang, D., Qi, S., et al.: A dynamic resource scheduling scheme in edge computing satellite networks. Mob. Netw. Appl. (2019)

    Google Scholar 

  19. Wang, F., Jiang, D., Qi, S.: An adaptive routing algorithm for integrated information networks. China Commun. 7(1), 196–207 (2019)

    Google Scholar 

  20. Huo, L., Jiang, D., Qi, S., et al.: An AI-based adaptive cognitive modeling and measurement method of network traffic for EIS. Mob. Netw. Appl. (2019)

    Google Scholar 

  21. Gao, Y.S., Bai, J., Wang, S.X., Zhao, J., Li, X.Y.: A sEMG recording system. Key Eng. Mater. 620, 465–470 (2014)

    Article  Google Scholar 

  22. Huo, L., Jiang, D., Lv, Z., et al.: An intelligent optimization-based traffic information acquirement approach to software-defined networking. Comput. Intell. 36, 1–21 (2019)

    Google Scholar 

  23. Huo, L., Jiang, D., Zhu, X., et al.: An SDN-based fine-grained measurement and modeling approach to vehicular communication network traffic. Int. J. Commun. Syst., 1–12 (2019)

    Google Scholar 

  24. Kim, M., Gu, G., Lee, W., Chung, W.K.: Pneumatic sleeve-assisted stable sEMG measurement for microneedle array electrode. In: 2018 15th International Conference on Ubiquitous Robots, pp. 1–4 (2018)

    Google Scholar 

  25. Zhang, K., Chen, L., An, Y., Cui, P.: A QoE test system for vehicular voice cloud services. Mob. Netw. Appl. (2019). https://doi.org/10.1007/s11036-019-01415-3

  26. Kim, J., André, E.: Emotion recognition based on physio-logical changes in music listening. IEEE Trans. Pattern Anal. Mach. Intell. 30(12), 2067–2083 (2008)

    Article  Google Scholar 

  27. Alja’afreh, M., Al Maadeed, S., Alja’am, J.M., El Saddik, A.: Towards a comprehensive study of fatigue deducing techniques for evaluating the quality of experience of haptic-visual applications. In: 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), Doha, Qatar, pp. 339–344 (2020)

    Google Scholar 

  28. Song, T., Zheng, W., Cheng, L., Zong, Y., Zhang, X., Cui, Z.: MPED: a multi-modal physiological emotion database for discrete emotion recognition. IEEE Access 7, 12177–12191 (2019)

    Article  Google Scholar 

  29. Wang, K., An, N., Li, B.N., et al.: Speech emotion recognition using fourier parameters. IEEE Trans. Affect. Comput. 6(1), 69–75 (2015)

    Article  Google Scholar 

  30. Zhang, T., Zheng, W., Cui, Z., et al.: A deep neural network driven feature learning method for multi-view facial expression recognition. IEEE Trans. Multimedia 18(12), 1 (2016)

    Article  Google Scholar 

  31. Kurniawan, H., Maslov, A., V., Pechenizkiy, M.: Stress detection from speech and galvanic skin response signals. In: IEEE International Symposium on Computer-Based Medical Systems. IEEE (2013)

    Google Scholar 

  32. Picard, R., Vyzas, E., Healey, J.: Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1175–1191 (2001)

    Article  Google Scholar 

  33. Chen, L., Zhang, L.: Spectral efficiency analysis for massive MIMO system under QoS constraint: an effective capacity perspective. Mob. Netw. Appl. (2020). https://doi.org/10.1007/s11036-019-01414-4

  34. Chen, L., Jiang, D., Bao, R., Xiong, J., Liu, F., Bei, L.: MIMO Scheduling effectiveness analysis for bursty data service from view of QoE. Chin. J. Electron. 26(5), 1079–1085 (2017)

    Article  Google Scholar 

  35. Chen, L., et al.: A lightweight end-side user experience data collection system for quality evaluation of multimedia communications. IEEE Access 6(1), 15408–15419 (2018)

    Article  Google Scholar 

  36. Rahimian, E., Zabihi, S., Atashzar, S.F., Asif, A., Mohammadi, A.: Semg-based hand gesture recognition via dilated convolutional neural networks. In: 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Ottawa, ON, Canada, pp. 1–5 (2019)

    Google Scholar 

  37. Wu, C., et al.: sEMG measurement position and feature optimization strategy for gesture recognition based on ANOVA and neural networks. IEEE Access 8, 56290–56299 (2020)

    Article  Google Scholar 

  38. Chaiyaroj, A., Sri-Iesaranusorn, P., Buekban, C., Dumnin, S., Thanawattano, C., Surangsrirat, D.: Deep neural network approach for hand, wrist, grasping and functional movements classification using low-cost sEMG sensors. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA, pp. 1443–1448 (2019)

    Google Scholar 

Download references

Acknowledgements

This work is partly supported by Jiangsu technology project of Housing and Urban-Rural Development (No. 2018ZD265, No. 2019ZD039, No. 2019ZD040, No. 2019ZD041).

Author information

Authors and Affiliations

  1. Jiangsu Province Key Laboratory of Intelligent Industry Control Technology, Xuzhou University of Technology, Xuzhou, 221018, China

    Jiamin Cheng, Yuan An, Ping Cui & Kailiang Zhang

Authors
  1. Jiamin Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuan An
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ping Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kailiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuan An .

Editor information

Editors and Affiliations

  1. Embry-Riddle Aeronautical University, Daytona Beach, FL, USA

    Houbing Song

  2. School of Astronautics and Aeronautic, UESTC, Chengdu, China

    Dingde Jiang

Rights and permissions

Reprints and permissions

Copyright information

© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cheng, J., An, Y., Cui, P., Zhang, K. (2021). Research on User Experience Quality Evaluation Method of Internet of Vehicles Based on sEMG Signal. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 369. Springer, Cham. https://doi.org/10.1007/978-3-030-72792-5_54

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-72792-5_54

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72791-8

  • Online ISBN: 978-3-030-72792-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation