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A novel algorithm to model the neuromuscular system from the eye to fingers to authenticate individuals through a typing process

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Abstract

The extensive use of computers has necessitated a new paradigm, in which computers are not only the major channel for dealing with day-to-day financial, industrial, and individual duties, but also the need to establish effective user identification for authentication reasons. Based on this fact, behavioral biometrics, such as typing, can be used for authentication to be subtle, unlike most biometrics.

In this paper, to verify the identity, an adaptive neuro-fuzzy inference system (ANFIS) is employed to model musculoskeletal system from the eye to the fingers in the typing process, as well as to model the control process of typing behavior. Model predictive control (MPC) is used to model the control process in order to get the best results. The improved distance evaluation (IDE) feature selection technique is utilized to minimize feature dimensions, and data fusion is conducted at the feature level. Besides, the Support Vector Machine (SVM) classifier is applied to authenticate selected features. Moreover, this algorithm is tested on a dataset of 35 users, providing accuracy with an Arithmetic mean of 99.65.

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Data Availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Code Availability

‘Not applicable’.

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Acknowledgements

We thank Hamed Ghatei Khiabani Azar for his sincere assistance and support.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering Central Tehran Branch, Islamic Azad University, Tehran, Iran

    Hajar Kavusi

  2. Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Keivan Maghooli

  3. Department of Biomedical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

    Siamak Haghipour

Authors
  1. Hajar Kavusi
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  2. Keivan Maghooli
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  3. Siamak Haghipour
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Hajar Kavusi], [Keivan Maghooli] and [Siamak Haghipour]. The first draft of the manuscript was written by [Hajar Kavusi] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Keivan Maghooli.

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Table 3 Minimum sample size for statistical power 80% [36]
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Kavusi, H., Maghooli, K. & Haghipour, S. A novel algorithm to model the neuromuscular system from the eye to fingers to authenticate individuals through a typing process. Electron Commer Res 25, 683–704 (2025). https://doi.org/10.1007/s10660-022-09594-0

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