Privacy-Aware Respiratory Symptom Detection
Low-sampling acoustic pipeline to detect coughs and sneezes in the wild with energy-efficient methods
Summary
I built a privacy-preserving, energy-efficient acoustic pipeline to detect coughs and sneezes at low sampling rates (1 kHz), suitable for deployment on resource-constrained devices.
Key contributions
- Designed feature extraction and decision-tree based classifiers that operate at 1 kHz sampling and preserve privacy by avoiding raw audio uploads.
- Validated the pipeline in-the-wild and achieved 70% sensitivity for cough detection and 57% sensitivity for sneeze detection.
- This work received the WellComp Best Paper award at UbiComp/ISWC 2023.
(No public repo available)