ScanRig-Edge: Leakage-Aware Browser-Native Temporal Pose Recognition for Real-Time Home Exercise Coaching

Real-time home exercise coaching requires recognition that is accurate under pose variation while remaining
efficient enough for client-side execution without continuous
cloud video processing. This paper presents ScanRig-Edge, a
browser-native home-exercise coaching application that integrates webcam-based MediaPipe pose tracking, temporal exercise
recognition, confidence-aware unknown rejection, and real-time
user feedback. Its recognition engine converts pose landmarks
into 48-frame normalized sequences and classifies nine exercises
plus an unknown class using a compact one-dimensional con
volutional network. A 2,200-sequence synthetic benchmark is
constructed from 80 persistent profiles with five profile-disjoint
split manifests and an integrity audit. The audit finds zero exact
duplicate sequences and zero train/validation/test profile overlap.
Under a five-seed matched-augmentation protocol, ScanRig-Edge
achieves a mean Macro-F1 of 99.53% with a standard deviation
of 0.33%, and a mean unknown-class F1 of 99.34% with a
standard deviation of 0.60% on post-freeze synthetic distribution
shifts. Hformer-A reaches the highest mean Macro-F1 of 99.92%,
but ScanRig-Edge achieves 0.595 ms median and 0.880 ms p95
browser inference under ONNX Runtime Web/WASM. These
measurements place ScanRig-Edge on the observed accuracy
latency Pareto frontier. The classifier is integrated into a working
React/Vite application with local inference, confidence-based
unknown rejection, model hot-reloading, and a privacy-conscious
pose-sequence collection workflow for future participant-disjoint
real-user validation.