On WESAD respiratory data under leave-one-subject-out validation, raw 1D-CNNs reach 96.72% accuracy for stress-vs-rest while grouped respiratory signatures yield higher MCC for baseline (65.34%), amusement (35.69%), and meditation (88.65%).
Combining multiple tiny ma- chine learning models for multimodal context-aware stress recogni- tion on constrained microcontrollers
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State-Specific Respiratory Signatures for Affective and Stress Recognition: Interpretable Respiratory Markers, Autocorrelation Lags, and Compact CNN Models
On WESAD respiratory data under leave-one-subject-out validation, raw 1D-CNNs reach 96.72% accuracy for stress-vs-rest while grouped respiratory signatures yield higher MCC for baseline (65.34%), amusement (35.69%), and meditation (88.65%).