GaitScreen: a ground reaction force-based framework for prescreening gait abnormalities in orthopaedic populations

Ground reaction force (GRF) trajectories capture kinetic alterations in stance-phase loading, propulsion, and mediolateral control. This study presents GaitScreen, a GRF-based framework for post-acquisition prioritisation of already recorded healthy-control and pathological-gait trials. GaitScreen converts vertical, anterior-posterior, and mediolateral stance-phase GRF trajectories into a review-prioritisation score and a threshold-dependent review flag. The framework combines a multiscale temporal backbone, a convolutional neural network component, and a logistic regression component to integrate multiscale force patterns, learned convolutional representations, and raw-waveform linear information. It was evaluated using the GaitRec dataset, comprising healthy controls and individuals with lower-limb orthopaedic conditions. Under a subject-exclusive evaluation protocol, GaitScreen achieved consistent trial-level performance across ten matched runs, with an accuracy of $0.8766\pm 0.0025$ , a macro-F1 score of $0.8757\pm 0.0024$ , and an AUROC of $0.9425\pm 0.0015$ . A high-sensitivity operating mode reduced the false-negative rate from 0.1736 to 0.1073, with an increase in false-positive review flags. The full hybrid model had a serialized size of 1.122 MB and a mean CPU inference latency of $0.626\pm 0.010$ ms per sample after GRF preprocessing. The compact model size and low inference latency support computationally scalable post-acquisition processing of standardised GRF trials.