Research article

Utilizing force and displacement in unnatural index finger movements for authentication

¹ Department of Biomedical Engineering, University of Isfahan, Iran
² School of Information Engineering, Jiangxi University of Science and Technology, No 86, Hongqi Ave, Ganzhou, 341000, Jiangxi, PR China
³ Aix Marseille University, CNRS, Centrale Med, M2P2, Marseille, France
⁴ School of Engineering, Infrastructure and Sustainability, Faculty of Technology, Arts, Culture, De Montfort University, Leicester, LE1 9BH, UK
⁵ Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy

^* e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 14 December 2025
Accepted: 7 January 2026

Abstract

The evolution of sensor technologies and real-time data processing has amplified the practicality of incorporating behavioral characteristics within security frameworks. Keystroke dynamics, in particular, has emerged as a prevalent behavioral biometric owing to the ubiquitous use of devices like mobile phones and computers, all reliant on password-based security systems. This study unveils an innovative authentication framework using leveraging deep learning algorithms, tapping into force and displacement data derived from the intricate abduction movements of the right index finger as a distinctive biometric trait. To ascertain its efficacy, we meticulously optimized this novel algorithm while benchmarking it against established deep learning models—Convolutional Neural Networks (CNNs), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), and one-dimensional CNN (1D-CNN). The subsequent evaluation encompassed a comprehensive comparative analysis of their performance metrics. The findings of this evaluation are compelling, demonstrating an average F1 score of 75.5% in validation data alongside an impressive average accuracy rate of 99.4%. These outcomes unequivocally highlight the precision and reliability inherent in utilizing force and displacement patterns as behavioral biometrics. Equally noteworthy is the system's display of a remarkably low False Acceptance Rate (FAR) of 0.27%, positioning it as a promising contender for seamless integration within advanced security systems. In essence, this research not only showcases the potential of leveraging nuanced behavioral traits but also emphasizes the practicality and robustness of employing force and displacement patterns as precise indicators in the realm of behavioral biometrics for enhanced system authentication and security.