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%).
A deep learning approach to stress recognition through multimodal physiological signal image transformation
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Wearable sensor data converted to visual embeddings and aggregated via attention MIL predicts perceived stress in elderly oncology patients with moderate accuracy (R² 0.24-0.28).
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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%).
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Stress Estimation in Elderly Oncology Patients Using Visual Wearable Representations and Multi-Instance Learning
Wearable sensor data converted to visual embeddings and aggregated via attention MIL predicts perceived stress in elderly oncology patients with moderate accuracy (R² 0.24-0.28).