AI 中文总结
该研究利用95名参与者的智能手机IMU数据，基于伊斯兰礼拜的结构化动作设计生物认证协议，通过两种运动表示实现了高准确率的身份验证，证实结构化礼拜动作可作为跨会话行为生物识别手段。
AI 中文摘要
伊斯兰礼拜是一种结构化的动作活动，为行为生物识别提供了独特场景：所有参与者执行相同的动作序列，因此必须从执行差异中推断身份。我们收集了95名参与者在夜间集体伊斯兰礼拜（塔拉威赫祈祷）期间使用自有智能手机采集的惯性数据。一个感知标签的流水线将长记录转换为结构完整的双拉卡（礼拜单元）行为样本，这使我们能够设计单元级和完整会话的行为生物识别协议。为解决礼拜者口袋中智能手机的任意朝向问题，我们研究了两种运动表示：具有重力相关加速度分量的旋转不变幅值，以及感知元数据的朝向（Qibla）参考规范化方法，该方法协调平台约定、重建设备到世界的姿态、通过WMM2025将Android磁北校正为真北，并在通用朝向-左-上坐标系中表达加速度和角速度。在单元级协议下，不变表示和朝向参考表示的成对随机森林（Random Forest）AUC/EER分别达到0.9932/4.07%和0.9923/3.96%；在完整会话留出测试下，分别为0.9700/7.62%和0.9618/7.81%。朝向框架的方向消融实验显示，完整六轴表示整体性能最强，加速度保留了大部分学习验证性能，垂直运动是最强的单轴线索。朝向参考的SimCLR实验在两次单元级运行中进一步产生参与者模板AUC为0.9805-0.9817，EER为5.81-6.24%。信号、描述符、礼拜组件的消融分析表明，参与者身份分布在整个运动动态中，而非集中在某一信号或姿势中。这些结果证实结构化礼拜动作是一种可测量的跨会话行为生物识别方式。
英文摘要
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.
Comments14 pagees, 3 figures, 9 tables, submitted to a suitable journal for possible publication
