Abstract :

The Increasing Deployment Of Smartwatches In Daily Activities—ranging From Health Monitoring And Communication To Financial Transactions— Demands Robust, User-friendly, And Continuous Authentication Mechanisms. Traditional PIN- Or Password-based Security Remains Insufficient Due To Usability Limitations And Vulnerability To Observational Attacks, As Highlighted In Behavioral Biometric Surveys [1]. Recent Advancements Show That Behavioral Signals Such As Gait Patterns [2], Wrist-worn Gait-based Authentication [3], Touch And Swipe Dynamics [4], And Keystroke Behavior On Mobile Devices [5] Offer Strong Discriminatory Features For Secure Identity Verification. Physiological Biometrics Such As Heart-rate Variability Captured Through PPG Sensors Have Also Shown Potential For Authentication In Wearable Environments [6]. Additionally, Smartwatch-specific Behavioral Cues—including Tapping Rhythms [7], Wristbased Gait Motion [8], And Multimodal Feature Integration [9–11]—have Been Recognized For Their High Effectiveness And Usability In Continuous Authentication Systems. Surveys On Touch Dynamics [12] And Gait-based Mobile Authentication [13] Further Support The Feasibility Of Behavioral Biometrics As A Reliable Replacement For Traditional Methods. However, Existing Single-modality Systems Often Suffer From Environmental Noise, Sensor Variability, And Spoofing Vulnerabilities. Comprehensive Reviews On Behavioral Biometrics [14] And Around-device Sensing Systems Such As SonarAuth [15] Emphasize The Need For Combining Multiple Modalities For Higher Robustness. Recent Smartwatch Studies On Pressure-based Biometric Input [16], Real-world Continuous Authentication [17], And Contextaware Mobile Authentication [18] Demonstrate That Multimodal Sensor Fusion Significantly Enhances Accuracy. Likewise, Machine-learningdriven Continuous Authentication Methods Using Typing And Motion Patterns [19], Along With Broader Reviews Of Behavioral Authentication For Security And Safety [20], Highlight The Value Of Integrated Behavioral Models. Motivated By These Findings, Wearable Wisdom: A Bi-Model Behavioral Biometric Scheme For Smart Watch User Authentication Introduces A Dual-modality Framework That Fuses Two Complementary Behavioral Signals—such As Gait, Wrist Movement, Touch Dynamics, Or Heart-rate Patterns—to Strengthen Real-time Authentication. By Leveraging Insights From The Referenced Literature [1–20], The Proposed System Provides A Secure, Unobtrusive, Continuous, And Contextaware Authentication Solution Suitable For Modern Smartwatch Ecosystems. Keywords : Behavioral Biometrics, Smartwatch Authentication, Wearable Security, Bi-Model Authentication, Gait Analysis, Wrist Motion Dynamics, Touch Interaction Patterns, Heart Rate Variability, Machine Learning, Continuous Authentication, Multimodal Biometrics, User Verification, Wearable Sensors, Spoof Resistance, Cybersecurity

Published:

29-11-2025

Issue:

Vol. 25 No. 11 (2025)

Page Nos:

286-293

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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