Prayer-Gait-Auth: Smartphone IMU-based Behavioral Biometrics from Structured Islamic Prayer Movements

Abstract

Islamic prayer is a structured movement activity that offers a distinctive setting for behavioral biometrics: all participants execute the same action sequence, so identity must be inferred from differences in execution. We collected inertial data from 95 participants using their own smartphones during nightly congregational Islamic prayer (Taraweeh). A label-aware pipeline converts long recordings into structurally complete two-rakaah behavioral samples (prayer units). This allows us to design unit-level and complete-session behavioral biometrics protocols. To address arbitrary smartphone orientation in worshippers' pockets, we study two motion representations: rotation-invariant magnitudes with gravity-relative acceleration components, and a metadata-aware Qibla-referenced canonicalization that harmonizes platform conventions, reconstructs device-to-world attitude, corrects Android magnetic north to true north via WMM2025, and expresses acceleration and angular velocity in a common Qibla-left-up frame. Under unit-level protocol, the invariant and Qibla-referenced representations reach learned pairwise Random Forest AUC/EER of 0.9932/4.07% and 0.9923/3.96%; under complete-session holdout, 0.9700/7.62% and 0.9618/7.81%. Qibla-frame directional ablations show the complete six-axis representation is strongest overall, with acceleration retaining most learned-verification performance and vertical motion the strongest single-axis cue. A Qibla-referenced SimCLR experiment further yields participant-template AUC 0.9805-0.9817 and EER 5.81-6.24% across two unit-level runs. Signal-, descriptor-, prayer-component ablation analyses show participant identity is distributed across movement dynamics rather than concentrated in one signal or posture. These results establish structured prayer movement as a measurable cross-session behavioral biometric.