Mobile authentication is essential for protecting personal data and payment authorization, yet prevailing approaches face two fundamental limitations: the nonrevocability of physiological biometrics once compromised and the susceptibility of knowledgebased authentication to observation- and trace-based attacks. This study proposes a next-generation mobile authentication framework based on dynamic behavioral profiling, in which binary pressure sequences and grip-position patterns are jointly modeled. The hardware design measures fine spatiotemporal pressure distributions using symmetrically mounted fabric-based strip sensor arrays along the display bracket, and enhances localized pressure-transfer efficiency via an electrode-aligned protrusion film. Closely coupled to this sensing design, the algorithm employs a probability-based soft-stacking scheme that combines class-wise predictive probabilities from base learners to mitigate information loss induced by threshold-based discretization, increasing mean accuracy from 0.802 to 0.857 compared with fixed-threshold encoding. The proposed framework achieves an AUC of 0.9867 (EER = 0.049) for 16-class pressure-sequence authentication and an AUC of 0.9870 (EER = 0.0595) for 6-class grip-scenario classification. These results support the feasibility of using touch-pressure and grip interactions as re-enrollable behavioral authentication signals and provide preliminary participant-level validation of the framework. Further investigation involving larger and more diverse populations and real-world settings is needed to assess its potential for continuous authentication.
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