Research LineSmart wearables |
Over 7% of the global population lives with a chronic respiratory disorder, which can significantly detriment individuals' quality of life by impairing sleep quality, contributing to anxiety, and increasing medical expenditures. Our work leverages noninvasive wearable biosensing tecnhologies and novel edge Artificial Intelligence (AI) methods to continuously monitor patients with such disorders throughout their daily lives. Our wearable device monitors patients' symptoms (i.e. coughing, wheezing) and analyzes breathing dynamics in order to provide valuable medical insights. Our edge AI algorithms both preserve patients' privacy by ensuring that none of their sensitive biodata ever leaves the device, and enable a long battery lifetime.
Keywords
Respiratory disorders, Chronic cough, Continuous patient monitoring, Edge Artificial Intelligent, Wearable medical devicesTeam
 | Albini Stefano |
 | Atienza Alonso David - Supervisor |
 | Orlandic Lara - Researcher |
 | Teijeiro Campo Tomas - Project coordinator deputy |
 | Thevenot Jérôme Paul Rémy - Project coordinator deputy |
 | Zhang Wensi |
Research Partners
 | CHUV |
Prototype of the BONSAI wearable respiratory disorder monitoring device
Related Publications
| Cough-E: A multimodal, privacy-preserving cough detection algorithm for the edge | ||||
| Albini, Stefano; Orlandic, Lara; Dan, Jonathan; Thevenot, Jerome; Teijeiro, Tomas; Constantinescu, Denisa-Andreea; Atienza, David | ||||
| 2025 | IEEE Journal of Biomedical and Health Informatics | |||
| How to Count Coughs: An Event-Based Framework for Evaluating Automatic Cough Detection Algorithm Performance | ||||
| Lara Orlandic, Jonathan Dan, Jerome Thevenot, Tomas Teijeiro, Alain Sauty, David Atienza | ||||
| 2024 | arXiv |  | ||
| A Multimodal Dataset for Automatic Edge-AI Cough Detection | ||||
| Orlandic, Lara; Thevenot, Jérôme Paul Rémy; Teijeiro, Tomas; Atienza Alonso, David | ||||
| 2023 | Conference Paper | |  | |