Related Experiment Video
Updated: Aug 1, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
Published on: July 22, 2025
Home monitoring in asthma: towards digital twins
David Drummond1, Jolt Roukema2, Mariëlle Pijnenburg3
1Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, Université Paris Cité, Inserm UMR 1138, HeKA team, Centre de Recherche des Cordeliers, Paris, France.
Purpose Of Review:
We highlight the recent advances in home monitoring of patients with asthma, and show that these advances converge towards the implementation of digital twin systems.
Recent Findings:
Connected devices for asthma are increasingly numerous, reliable and effective: new electronic monitoring devices extend to nebulizers and spacers, are able to assess the quality of the inhalation technique, and to identify asthma attack triggers when they include a geolocation function; environmental data can be acquired from databases and refined by wearable air quality sensors; smartwatches are better validated. Connected devices are increasingly integrated into global monitoring systems. At the same time, machine learning techniques open up the possibility of using the large amount of data collected to obtain a holistic assessment of asthma patients, and social robots and virtual assistants can help patients in the daily management of their asthma.
Summary:
Advances in the internet of things, machine learning techniques and digital patient support tools for asthma are paving the way for a new era of research on digital twins in asthma.
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