Data-Minimizing Motion-Gated Artificial Intelligence Decision Support for Electric Micro-Mobility: An Architecture-Feasibility Study.

Physiological sensing can support electric micro-mobility safety research. Elevated heart rate during riding, however, may reflect physical exertion, psychological stress, motion artefacts, road context, or several of these factors. This paper presents RideSafe-AI, a non-clinical architecture-feasibility prototype. It separates data quality, exertion/load proxy estimation, public-dataset stress-label classification, and motion reliability before applying deterministic fusion and abstention. The Python reference checks input and output fields for raw physiological streams and direct identifiers, while the ESP32-S3 (Espressif Systems, Shanghai, China) checks incoming fields; Android constructs bounded inputs internally without an equivalent schema guard. PAMAP2 supports exertion/load proxy modelling, WESAD supports low/high laboratory condition-label classification, and official PhysioNet stress/exercise records support a subject-wise laboratory feature-set experiment. On the PhysioNet binary stress-versus-exercise task, full-feature XGBoost achieved 0.963 accuracy, 0.944 macro-F1, and a 0.015 exercise false-stress rate on 10,824 windows from 36 subjects. These results describe that public-dataset task; PhysioNet does not contain bike-frame motion, GPS terrain, or power telemetry and does not evaluate the complete RideSafe-AI pipeline. Deterministic scenarios verify selected decision rules, while two same-day technical rides by one participant demonstrate prototype integration and logging. These findings support architecture feasibility. Rider-stress accuracy, safety impact, and performance across riders and road conditions remain to be evaluated.