An Indoor Accessibility Assessment Framework Based on Multimodal Sensing and Explainable Machine Learning: A Case...

As accessibility development in public buildings has gradually shifted from facility compliance toward experience- and performance-oriented evaluation, the quantitative assessment of indoor mobility experiences among blind users still lacks a systematic sensor-supported analytical framework. To address this gap, this study proposes an indoor accessibility assessment approach that integrates multi-sensor data acquisition with explainable machine learning, using a tactile museum as the experimental setting. Sixty-four participants with first-level blindness were recruited to complete a real-world directed walking task. A multimodal database was constructed by integrating objective data collected from an ultra-wideband (UWB) indoor positioning system, an intelligent gait analysis system, and video-based behavioral recording, including spatiotemporal trajectories, gait characteristics, and behavioral events, together with post-task accessibility satisfaction ratings. Based on this dataset, a random forest model was developed using the Overall Accessibility Satisfaction Score (OAS) as the response variable. SHAP, partial dependence analysis, and GAM smoothing were further applied to interpret the associations between key variables and predicted satisfaction. The results showed that walking distance, number of turns, self-reported collision perception, and selected gait indicators made relatively high contributions to the model interpretation, and these variables exhibited certain nonlinear associations with predicted satisfaction. These findings suggest that combining multi-source sensor-based behavioral measurement with explainable machine learning has potential for sensor-supported post-occupancy evaluation of indoor accessibility environments and can provide exploratory references for the quantitative assessment and optimization of accessibility in public buildings.