Skip to main content
SpringerLink
Log in

User Experience Quality Analysis Method Based on Frequency Domain Characteristics of Physiological Signal

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

Included in the following conference series:

Abstract

In recent years, the development of intelligent driving is rapid, the related business is constantly upgrading, and the end-user service is becoming more and more perfect. From the perspective of end users, obtaining the evaluation results of experience quality is an effective way to enhance the core competitiveness of business. In this paper, the mapping method of user experience quality is established based on the frequency domain characteristics of SEMG signal, so as to obtain the current real experience quality of intelligent driving terminal users. Data analysis shows that this method can effectively obtain the quality of real user experience. This study can be used as reference data to improve the business experience of intelligent driving terminal users, improve the relevant technical parameters, and enhance the core competitiveness.

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

Access this chapter

Log in via an institution
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

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. 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 Communi. (2019)

    Google Scholar 

  3. 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 

  4. 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 

  5. 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 

  6. 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 

  7. 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 

  8. 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 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  13. 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 

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

    Google Scholar 

  15. 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. 33, 1–12 (2019)

    Google Scholar 

  16. Song, H.Y., Zhang, J.G., Wang, J., et al.: Study on muscle fatigue property of human body in shoulder loaded walking based on surface electromyogram. J. Biomed. Eng. 33(3), 426–430 (2016)

    Google Scholar 

  17. Wu, Q., Chen, X., Ding, L., et al.: Classification of EMG signals by BFA-optimized GSVCM for diagnosis of fatigue statu. IEEE Trans. Autom. Sci. Eng. 14(2), 915–930 (2017)

    Article  Google Scholar 

  18. Liu, J., Zou, R.L., Zhang, D.H., et al.: Analysis of the muscle fatigue based on band spectrum entropy of multi-channel surface electromyography. J. Biomed. Eng. 33(3), 431–435 (2016)

    Google Scholar 

  19. Wang, K.: Conventional time-frequency method of SEMG and strategy used for dynamic muscle fatigue analysis. Chin. J. Sports Med. 29(1), 104–108 (2010)

    Google Scholar 

  20. Naeem, U.J., Xiong, C.H.: FFM: a muscle fatigue index extraction by utilizing fuzzy network and mean power frequency. Int. J. Eng. Bus. Enterp. Appl. 3(1), 25–35 (2013)

    Google Scholar 

  21. Chowdhury, R., Reaz, B.M.I., Islam, M.T.: Wavelet transform to recognize muscle fatigue. In: Proceedings of the 2012 Third Asian Himalayas International Conference on Internet, pp. 1–5. IEEE, Piscataway (2012)

    Google Scholar 

  22. Li, Z., Wang, B., Yang, C., et al.: Boosting-based EMG patterns classification scheme for robustness enhancement. IEEE J. Biomed. Health Inf. 17(3), 545–552 (2013)

    Article  MathSciNet  Google Scholar 

  23. 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

  24. 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 

  25. Husič, J.B., Baraković, S., Muminović, S.: Is there any impact of human influence factors on quality of experience? In: 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, pp. 434–439 (2017)

    Google Scholar 

  26. De Moor, K., Arndt, S., Ammar, D., Voigt-Antons, J., Perkis, A., Heegaard, P.E.: Exploring diverse measures for evaluating QoE in the context of WebRTC. In: 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX), Erfurt, pp. 1–3 (2017)

    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. Fall, C.L., et al.: Wireless SEMG-based body-machine interface for assistive technology devices. IEEE J. Biomed. Health Inf. 21(4), 967–977 (2017)

    Article  Google Scholar 

  29. Zhang, M., Zhang, W., Zhang, B., Wang, Y., Li, G.: Feature selection of mime speech recognition using surface electromyography data. In: 2019 Chinese Automation Congress (CAC), Hangzhou, China, pp. 3173–3178 (2019)

    Google Scholar 

  30. Hulliyah, K., Bakar, N.S.A.A., Ismail, A.R.: Emotion recognition and brain mapping for sentiment analysis: a review. In: 2017 Second International Conference on Informatics and Computing (ICIC), Jayapura, pp. 1–5 (2017)

    Google Scholar 

  31. Tzirakis, P., Zhang, J., Schuller, B.W.: End-to-end speech emotion recognition using deep neural networks. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, pp. 5089–5093 (2018)

    Google Scholar 

  32. Zhao, W., Zhao, Z., Li, C.: Discriminative-CCA promoted By EEG signals for physiological-based emotion recognition. In: 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia), Beijing, pp. 1–6 (2018)

    Google Scholar 

  33. Chettupuzhakkaran, P., Sindhu, N.: Emotion recognition from physiological signals using time-frequency analysis methods. In: 2018 International Conference on Emerging Trends and Innovations in Engineering And Technological Research (ICETIETR), Ernakulam, pp. 1–5 (2018)

    Google Scholar 

  34. Işik, Ü., Güven, A.: Classification of emotion from physiological signals via artificial intelligence techniques. In: 2019 Medical Technologies Congress (TIPTEKNO), Izmir, Turkey, pp. 1–4 (2019)

    Google Scholar 

  35. Widanti, N., Sumanto, B., Rosa, P., Miftahudin, M.F.: Stress level detection using heart rate, blood pressure, and GSR and stress therapy by utilizing infrared. In: 2015 International Conference on Industrial Instrumentation and Control (ICIC), Pune, pp. 275–279 (2015)

    Google Scholar 

  36. Al Jaafreh, M., Hamam, A., El Saddik, A.: A framework to analyze fatigue for haptic-based tactile internet applications. In: 2017 IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE), Abu Dhabi, pp. 1–6 (2017)

    Google Scholar 

  37. 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 

  38. Zhang, K., Chen, L., An, Y., et al.: A QoE test system for vehicular voice cloud services. Mob. Netw. Appl. (2020). https://doi.org/10.1007/s11036-019-01415-3

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

    Ke Sun, Yuan An, Kailiang Zhang & Ping Cui

Authors
  1. Ke Sun
    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. Kailiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ping Cui
    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

Sun, K., An, Y., Zhang, K., Cui, P. (2021). User Experience Quality Analysis Method Based on Frequency Domain Characteristics of Physiological 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_56

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-72792-5_56

  • 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