A Transformer model predicts personalized post-intervention trajectories and direction of change for heart rate and variability from wearable sensor data, showing feasibility as a proof of concept.
Detec- tion and monitoring of stress using wearables: A systematic review
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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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Personalized and Context-Aware Transformer Models for Predicting Post-Intervention Physiological Responses from Wearable Sensor Data
A Transformer model predicts personalized post-intervention trajectories and direction of change for heart rate and variability from wearable sensor data, showing feasibility as a proof of concept.
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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).