ICASSP 2022accepted0 citations

Coughtrigger: Earbuds IMU Based Cough Detection Activator Using An Energy-Efficient Sensitivity-Prioritized Time Series Classifier

Shibo Zhang, Ebrahim Nemati, Minh Dinh, Nathan Folkman, Tousif Ahmed, Md. Mahbubur Rahman, Jilong Kuang, Nabil Alshurafa

Abstract

Persistent coughs are a major symptom of respiratory-related diseases. Increasing research attention has been paid to detecting coughs using wearables, especially during the COVID-19 pandemic. Microphone is most widely used sensor to detect coughs. However, the intense power consumption needed to process audio hinders continuous audio-based cough detection on battery-limited commercial wearables, such as earbuds. We present CoughTrigger, which utilizes a lower-power sensor, inertial measurement unit (IMU), in earbuds as a cough detection activator to trigger a higher-power sensor for audio processing and classification. It runs all-the-time as a standby service with minimal battery consumption and triggers the audio-based cough detection when a candidate cough is detected from IMU. Besides, the use of IMU brings the benefit of improved specificity of cough detection. Experiments are conducted on 45 subjects and CoughTrigger achieved 0.77 AUC score. We also validated its effectiveness on free-living data and through on-device implementation.

BibTeX
@inproceedings{icassp2022_coughtriggerearb,
  title = {Coughtrigger: Earbuds IMU Based Cough Detection Activator Using An Energy-Efficient Sensitivity-Prioritized Time Series Classifier},
  author = {Shibo Zhang and Ebrahim Nemati and Minh Dinh and Nathan Folkman and Tousif Ahmed and Md. Mahbubur Rahman and Jilong Kuang and Nabil Alshurafa and Alex Gao},
  booktitle = {ICASSP 2022},
  year = {2022}
}