Cough and sneeze are the most common means to spread respiratory diseases amongst humans. Existing approaches to detect coughing and sneezing events are either intrusive or do not provide any reliability measure. This paper offers a novel proposal to reliably and non-intrusively detect such events using a smartwatch as the underlying hardware, Conformal Prediction as the underlying software. We rigorously analysed the performances of our proposal with the Harvard ESC Environmental Sound dataset, and real coughing samples taken from a smartwatch in different ambient noises.
|Published - Jun 2018
|7th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2018). - , Netherlands
Duration: 11 Jun 2018 → 13 Jun 2018
|7th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2018).
|11/06/18 → 13/06/18