Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Using Keytyping as a Biomarker for Cognitive Decline Diagnostics: The Convolutional Neural Network Based Approach

  • Conference paper
  • First Online:
Pattern Recognition and Artificial Intelligence (MedPRAI 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1543))

Abstract

Parkinson’s disease (PD) can cause many motor impairments in humans such as muscle rigidity/stiffness, hand tremors, etc., causing difficulty when interacting with computer input devices. The purpose of this work was to classify signals obtained from keytyping using wavelet features and deep learning. We proposed a unique technique for diagnosing PD utilizing data-derived scalograms and categorizing them using a custom 10-layer CNN model. The scalograms are created using the wavelet coefficients at different scales. The classification of PD vs. healthy subjects produced results equivalent to most cutting-edge methods, with an accuracy of 93.30%. Our method, which is based on the study of temporal patterns from ordinary interactions with electronic devices, allows us to objectively detect motor impairment in PD patients while they type on a computer at home or at work.

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 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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. Priya, S.J., Rani, A.J., Subathra, M.S.P., Mohammed, M.A., Damaševičius, R., Ubendran, N.: Local pattern transformation based feature extraction for recognition of Parkinson’s disease based on gait signals. Diagnostics 11(8), 1395 (2021)

    Article  Google Scholar 

  2. Guimaraes, M.T., et al.: An optimized approach to Huntington’s disease detecting via audio signals processing with dimensionality reduction. In: International Joint Conference on Neural Networks (2020)

    Google Scholar 

  3. Lauraitis, A., Maskeliunas, R., Damaševičius, R., Krilavičius, T.: Detection of speech impairments using cepstrum, auditory spectrogram and wavelet time scattering domain features. IEEE Access 8, 96 162–96 172 (2020)

    Google Scholar 

  4. Giancardo, L., et al.: Computer keyboard interaction as an indicator of early Parkinson’s disease. Sci. Rep. 6(1), 1–10 (2016)

    Article  Google Scholar 

  5. Goetz, C.G., et al.: Movement disorder society-sponsored revision of the unified Parkinson’s disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov. Disord. 23(15), 2129–2170 (2008)

    Article  Google Scholar 

  6. Miller, D.B., O’Callaghan, J.P.: Biomarkers of Parkinson’s disease: present and future. Metabolism 64(3), S40–S46 (2015)

    Article  Google Scholar 

  7. Crawford, T., Goodrich, S., Henderson, L., Kennard, C.: Predictive responses in Parkinson’s disease: Manual keypresses and saccadic eye movements to regular stimulus events. J. Neurol. Neurosurg. Psychiatry 52(9), 1033–1042 (1989)

    Article  Google Scholar 

  8. Vanagas, G., Engelbrecht, R., Damaševičius, R., Suomi, R., Solanas, A.: Ehealth solutions for the integrated healthcare. J. Healthcare Eng. vol. 2018 (2018)

    Google Scholar 

  9. Byeon, Y.-H., Pan, S.-B., Kwak, K.-C.: Intelligent deep models based on scalograms of electrocardiogram signals for biometrics. Sensors 19(4), 935 (2019)

    Article  Google Scholar 

  10. Růžička, E., Krupička, R., Zárubová, K., Rusz, J., Jech, R., Szabó, Z.: Tests of manual dexterity and speed in Parkinson’s disease: not all measure the same. Parkinsonism Rel. Disord. 28, 118–123 (2016)

    Article  Google Scholar 

  11. Adams, W.R.: High-accuracy detection of early Parkinson’s disease using multiple characteristics of finger movement while typing. PLoS ONE 12(11), 12 (2017)

    Article  Google Scholar 

  12. Ulinskas, M., Woźniak, M., Damaševičius, R.: Analysis of keystroke dynamics for fatigue recognition. In: Gervasi, O., et al. (eds.) ICCSA 2017. LNCS, vol. 10408, pp. 235–247. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62404-4_18

    Chapter  Google Scholar 

  13. Arroyo-Gallego, T., et al.: Detection of motor impairment in Parkinson’s disease via mobile touchscreen typing. IEEE Trans. Biomed. Eng. 64(9), 1994–2002 (2017)

    Article  Google Scholar 

  14. Arroyo-Gallego, T., et al.: Detecting motor impairment in early Parkinson’s disease via natural typing interaction with keyboards: validation of the neuroqwerty approach in an uncontrolled at-home setting’’. J. Med. Internet Res. 20(3), e9462 (2018)

    Article  Google Scholar 

  15. Shribman, S., Hasan, H., Hadavi, S., Giovannoni, G., Noyce, A.J.: The brain test: a keyboard-tapping test to assess disability and clinical features of multiple sclerosis. J. Neurol. 265(2), 285–290 (2018)

    Article  Google Scholar 

  16. Iakovakis, D., Hadjidimitriou, S., Charisis, V., Bostantzopoulou, S., Katsarou, Z., Hadjileontiadis, L.J.: Touchscreen typing-pattern analysis for detecting fine motor skills decline in early-stage Parkinson’s disease. Sci. Rep. 8(1), 1–13 (2018)

    Article  Google Scholar 

  17. Pham, T.D., Wardell, K., Eklund, A., Salerud, G.: Classification of short time series in early Parkinson’s disease with deep learning of fuzzy recurrence plots. IEEE/CAA J. Automatica Sinica 6(6), 1306–1317 (2019)

    Article  MathSciNet  Google Scholar 

  18. Trager, M.H., Wilkins, K.B., Koop, M.M., Bronte-Stewart, H.: A validated measure of rigidity in Parkinson’s disease using alternating finger tapping on an engineered keyboard. Parkinsonism Relat. Disord. 81, 161–164 (2020)

    Article  Google Scholar 

  19. Abayomi-Alli, O.O., Damasevicius, R., Maskeliunas, R., Abayomi-Alli, A.: Bilstm with data augmentation using interpolation methods to improve early detection of Parkinson disease. In: Federated Conference on Computer Science and Information Systems, FedCSIS 2020, pp. 371–380 (2020)

    Google Scholar 

  20. Peachap, A.B., Tchiotsop, D., Louis-Dorr, V., Wolf, D.: Detection of early Parkinson’s disease with wavelet features using finger typing movements on a keyboard. SN Appl. Sci. 2(10), 1–8 (2020)

    Article  Google Scholar 

  21. Min, O., Wei, Z., Nian, Z., Su, X.: An application of LSTM prediction model based on keystroke data. In: ACM International Conference Proceeding Series (2020)

    Google Scholar 

  22. Akram, N., et al.: Developing and validating a new web-based tapping test for measuring distal bradykinesia in Parkinson’s disease’ (2020)

    Google Scholar 

  23. Wang, Y., et al.: Facilitating text entry on smartphones with qwerty keyboard for users with Parkinson’s disease. In: Conference on Human Factors in Computing Systems (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Informatics, Kaunas University of Technology, Studentu 50, Kaunas, Lithuania

    Lucas Salvador Barnardo, Robertas Damasevicius & Rytis Maskeliunas

Authors
  1. Lucas Salvador Barnardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robertas Damasevicius
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rytis Maskeliunas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Robertas Damasevicius .

Editor information

Editors and Affiliations

  1. Larbi Tebessi University, Tebessa, Algeria

    Chawki Djeddi

  2. Bahria University, Islamabad, Pakistan

    Imran Siddiqi

  3. FAST National University, Islamabad, Pakistan

    Akhtar Jamil

  4. Istanbul Sabahattin Zaim University, Istanbul, Turkey

    Alaa Ali Hameed

  5. Istanbul Sabahattin Zaim University, Istanbul, Turkey

    İsmail Kucuk

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Barnardo, L.S., Damasevicius, R., Maskeliunas, R. (2022). Using Keytyping as a Biomarker for Cognitive Decline Diagnostics: The Convolutional Neural Network Based Approach. In: Djeddi, C., Siddiqi, I., Jamil, A., Ali Hameed, A., Kucuk, İ. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2021. Communications in Computer and Information Science, vol 1543. Springer, Cham. https://doi.org/10.1007/978-3-031-04112-9_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-04112-9_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04111-2

  • Online ISBN: 978-3-031-04112-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation