Deep learning-enabled block scrambling algorithm for securing telemedicine data of table tennis players (original) (raw)

Abstract

In sports, advance sensing technologies generate massive amount of unstructured telemedicine data that need to be refined for accurate diagnosis of underlying diseases. For accurate prediction of diseases and classification of athletes’ data, deep learning algorithms are frequently used at the cloud. However, the transmission of raw data of athletes to the cloud faces numerous challenges. Among them, security and privacy are a major challenge in view of the sensitive and personal information present within the unstructured data. In this paper, first we present a data block scrambling algorithm (without key management) for secured transmission and storage of ECG (electrocardiogram) data of table tennis players at the cloud. A small piece of original data stored at the cloud is used for scrambling the massive amount of remaining ECG data. The secured telemedicine data is then imported into Hadoop Distributed File System for data management, which is read by Spark framework to form Resilient Distributed Datasets. Finally, a deep learning approach is used that extracts useful features, learns the related information, and weights and sums the feature vectors at different layers for classification. Theoretical analysis proves that our proposed approach is highly robust and resilient to brute force attacks and at the same time has a much better accuracy, sensitivity, and specificity as compared to the existing approaches.

Access this article

Log in via an institution

Subscribe and save

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Hou R, Kong Y, Cai B, Liu H (2020) Unstructured big data analysis algorithm and simulation of Internet of Things based on machine learning. Neural Comput Appl 32(10):5399–5407
    Article Google Scholar
  2. Mainetti L, Patrono L, Stefanizzi ML (2016) An Internet of sport architecture based on emerging enabling technologies. In: International Multidisciplinary Conference on Computer and Energy Science (SpliTech). IEEE, pp 1–6
  3. Liang H (2021) Evaluation of fitness state of sports training based on self-organizing neural network. Neural Comput Appl 1–13
  4. Kallipolitis A, Galliakis M, Menychtas A, Maglogiannis I (2020) Affective analysis of patients in homecare video-assisted telemedicine using computational intelligence. Neural Comput Appl 32(23):17125–17136
    Article Google Scholar
  5. Ma H, Pang X (2019) Research and analysis of sport medical data processing algorithms based on deep learning and Internet of Things. IEEE Access 7:118839–118849
    Article Google Scholar
  6. Wang H, Dong C, Fu Y (2020) Optimization analysis of sport pattern driven by machine learning and multi-agent. Neural Comput Appl 1–11
  7. Usman M, Jolfaei A, Jan MA (2020) RaSEC: an intelligent framework for reliable and secure multilevel edge computing in industrial environments. IEEE Trans Ind Appl 56(4):4543–4551
    Google Scholar
  8. Qiu H, Qiu M, Lu Z (2020) Selective encryption on ECG data in body sensor network based on supervised machine learning. Inform Fus 55:59–67
    Article Google Scholar
  9. Devriendt T, Chokoshvili D, Favaretto M, Borry P (2018) Do athletes have a right to access data in their Athlete Biological Passport? Drug Test Anal 10(5):802–806
    Article Google Scholar
  10. Rathore H, Mohamed A, Guizani M, Rathore S (2021) Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach. Neural Comput Appl 1–14
  11. De Leeuw AW, van der Zwaard S, van Baar R, Knobbe A (2021) Personalized machine learning approach to injury monitoring in elite volleyball players. Eur J Sport Sci 1–14
  12. Oliver JL, Ayala F, Croix MBDS, Lloyd RS, Myer GD, Read PJ (2020) Using machine learning to improve our understanding of injury risk and prediction in elite male youth football players. J Sci Med Sport 23(11):1044–1048
    Article Google Scholar
  13. Tang D (2020) Hybridized hierarchical deep convolutional neural network for sports rehabilitation exercises. IEEE Access 8:118969–118977
    Article Google Scholar
  14. Yu H (2020) Research and optimization of sports injury medical system under the background of Internet of things. Trans Emerg Telecommun Technol 31(12):e3929
    Google Scholar
  15. Hatamzadeh M, Hassannejad R, Sharifnezhad A (2020) A new method of diagnosing athlete’s anterior cruciate ligament health status using surface electromyography and deep convolutional neural network. Biocybern Biomed Eng 40(1):65–76
    Article Google Scholar
  16. Yuan C, Yang Y, Liu Y (2020) Sports decision-making model based on data mining and neural network. Neural Comput Appl 1–14
  17. Chen H, Liu C (2020) Research on knee injuries in college football training based on artificial neural network. In: IEEE conference on telecommunications, optics and computer science (TOCS). IEEE, pp 35–37
  18. Saheb T, Izadi L (2019) Paradigm of IoT big data analytics in the healthcare industry: A review of scientific literature and mapping of research trends. Telemat Inform 41:70–85
    Article Google Scholar
  19. Rezaeibagha F, Mu Y (2018) Practical and secure telemedicine systems for user mobility. J Biomed Inform 78:24–32
    Article Google Scholar
  20. Aiyegbusi A, Oduntan M (2020) The relationship between grip styles and musculoskeletal injuries in table tennis players in Lagos, Nigeria: a cross-sectional study. J Clin Sci 17(3):52–61
    Article Google Scholar
  21. Rahardja U, Hardini M, Al Nasir AL, Aini Q (2020) Taekwondo sports test and training data management using blockchain. In: 5th International conference on informatics and computing (ICIC). IEEE, pp 1–6
  22. Liu J, Tang H, Sun R, Du X, Guizani M (2019) Lightweight and Privacy-Preserving Medical Services Access for Healthcare Cloud. IEEE Access 7:106951–106961
    Article Google Scholar
  23. Yu X, Jiang F, Du J, Gong D (2019) A cross-domain collaborative filtering algorithm with expanding user and item features via the latent factor space of auxiliary domains. Pattern Recogn 94:96–109
    Article Google Scholar
  24. Ning X, Gong K, Li W, Zhang L (2020) JWSAA: joint weak saliency and attention aware for person re-identification. Neurocomputing
  25. Yu X, Chu Y, Jiang F, Guo Y, Gong D (2018) SVMs classification based two-side cross domain collaborative filtering by inferring intrinsic user and item features. Knowl-Based Syst 141:80–91
    Article Google Scholar
  26. Cai W, Liu B, Wei Z, Li M, Kan J (2021) TARDB-Net: triple-attention guided residual dense and BiLSTM networks for hyperspectral image classification. Multimed Tools Appl 1–22
  27. Wang Z, Zou C, Cai W (2020) Small sample classification of hyperspectral remote sensing images based on sequential joint deeping learning model. IEEE Access 8:71353–71363
    Article Google Scholar
  28. Yu X, Yang J, Xie Z (2014) Training SVMs on a bound vectors set based on Fisher projection. Front Comput Sci 8(5):793–806
    Article MathSciNet Google Scholar
  29. Huang L, Xie G, Blenkinsopp J, Huang R, Bin H (2020) Crowdsourcing for sustainable urban logistics: Exploring the factors influencing crowd Workers’ participative behavior. Sustainability 12(8):3091
    Article Google Scholar

Download references

Acknowledgement

This work was supported by the Scientific Research Start Fund for high-level talents of Yulin Normal University under Grant G2020SK18.

Author information

Authors and Affiliations

  1. Department of Physical Education and Health, Zhaoqing University, Zhaoqing, 526061, Guangdong, China
    Bo Yang
  2. Department of Physical Education, Guangzhou Sport University, Guangzhou, 510500, Guangdong, China
    Bojin Cheng
  3. Zhaoqing Engineering Technical School, Youth League Committee, Zhaoqing, 526061, Guangdong, China
    Yixuan Liu
  4. Institute of Physical Education and Health, Yulin Normal University, Yulin, 537000, China
    Lijun Wang
  5. Institute of Physical Education, Soochow University, SuZhou, 215000, JiangSu, China
    Lijun Wang

Authors

  1. Bo Yang
  2. Bojin Cheng
  3. Yixuan Liu
  4. Lijun Wang

Corresponding author

Correspondence toLijun Wang.

Ethics declarations

Conflict of interest

The authors have no conflict of interest for publication of this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

About this article

Cite this article

Yang, B., Cheng, B., Liu, Y. et al. Deep learning-enabled block scrambling algorithm for securing telemedicine data of table tennis players.Neural Comput & Applic 35, 14667–14680 (2023). https://doi.org/10.1007/s00521-021-05988-7

Download citation

Keywords