arXiv (OAI Expanded)
open access
State-specific respiratory signatures for affective and stress recognition: Interpretable respiratory markers, autocorrelation lags, and compact CNN models
Respiratory activity is a direct and interpretable physiological channel for wearable stress and affective-state recognition, yet many studies emphasize classification accuracy without identifying which respiratory properties separate different states. Using the chest respiratory channel of the WESA
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Interpretable Stress Detection from ECG Signals Using Motif-Based Anomaly Analysis#1036862
- Deep Learning for Affect Recognition: A Comparative Study of Physiological Sensor-Based and Facial Image-Based Approaches#332989
- Reconhecimento de expressões faciais usando deep learning: uma análise comparativa sistemática em cenários de mundo real#1041410
- Deep learning-based emotion recognition using unimodal facial expressions or physiological signals: A review#363850
- Deep learning for stress oriented human activity recognition.#251332
Record · ID 1069839
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive