ArticleSensors (Basel, Switzerland)2026
Data-Minimizing Motion-Gated Artificial Intelligence Decision Support for Electric Micro-Mobility: An Architecture-Feasibility Study.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
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.
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