Condition-Wise Robustness of Skeleton-Based Gait Sex Classification Under Smartphone Use, Occlusion, and Speed Variations

Skeleton-based gait sex classification can reduce reliance on appearance cues, yet its robustness under everyday walking disturbances remains under-quantified. Using PsyMo 2D pose sequences (90° side view), we render Common Objects in Context (COCO) keypoints into compact grayscale skeleton images, segment sequences into fixed-length 15-frame clips, and classify them with a 3D residual convolutional neural network (CNN) under a subject-wise split shared across four aggregated conditions: overall (A), occlusion/carrying disturbance (B), speed variation (C), and smartphone use (D). To avoid an arbitrary decision rule, we select a global operating threshold on the validation set by sweeping τ to maximize macro-F1, apply it unchanged to the held-out test set, and report a threshold-sensitivity check. Robustness is audited via condition-wise confusion matrices, subgroup precision/recall with 95% subject-level bootstrap confidence intervals, and subject-level probability overlap. To contextualize condition-dependent behavior, we quantify joint-group attribution shifts using Gradient-weighted Class Activation Mapping (Grad-CAM) and examine a coarse arm-swing proxy under smartphone use. Subject-level test accuracy ranged from 0.761 to 0.870 across conditions A–D, with uncertainty summarized by 95% subject-level bootstrap confidence intervals; performance was lowest in B, with increased male→female errors. Overall, these results provide a transparent audit-and-interpretation framework for assessing skeleton-based gait sex classification under realistic walking perturbations in practical evaluation scenarios.