Multimodal Transformer-Based Gait Analysis and Deep Learning Model for Slip-Resistant Footwear Evaluation

Slips and falls remain a leading cause of injury, particularly in icy winter conditions and occupational settings with slippery surfaces. Several laboratory and field-based systems are designed to assess footwear slip resistance, including mechanical testers, tribometers, and human-centred gait-based assessments. While mechanical tests offer standardized slip resistance metrics for footwear, they often fail to reflect real-world variability in human gait, environmental conditions, and anthropometric diversity. On the other hand, although human-centered testing ensures ecological validity, it typically reports a single aggregated score for footwear derived from multiple participants, which obscures individual biomechanical differences and limits the ability to provide personalized assessments of slip resistance performance. This study introduces a transformer-based regression model that integrates (1) temporal biomechanical features, (2) outsole images, (3) footwear metadata, and (4) anthropometric data to predict personalized slip resistance score of 84 footwear across 578 walking trials conducted by 18 human participants on two types of ice: wet and dry. The predictive performance of our multimodal deep learning model varied by surface condition, with substantially better results on dry surfaces (RMSE: 1.34° ± 0.34°, R2: 0.45 ± 0.09, MAPE: 10.48% ± 3.58%) than on wet surfaces (RMSE: 2.34° ± 0.43°, R2: 0.47 ± 0.12, MAPE: 21.45% ± 6.89%). The results demonstrate the potential of fusing biomechanical and outsole characteristics to reliably assess slip risk under varying environmental conditions. Additionally, they emphasize the need for adaptive and context-aware testing protocols and pave the way for the development of intelligent footwear assessment tools aimed at injury prevention.