Asthma Prediction Using Vowel Inspiration
Sandhya Prasad, Asim Bhaumik, Suvidha Rupesh Kumar, L. Rama Parvathy, Heshalini Rajagopal, Saeed Janani · 2025
The most common respiratory disorder that affects millions of individuals yearly is asthma. Traditional technologies used for asthma detection requires patients to breathe in and breathe out, which may place an extra burden on the patients. The developments in the field of machine learning and mobile computing have enabled the research community to provide a simple and easy solution to monitor pulmonary disorders, reducing the additional burden on asthmatic patients. The proposed work is a small step towards the early detection and monitoring of asthma using a mobile device, which can capture the speech signal. This would alleviate the additional effort by asthmatic patients during the diagnosis process and would reduce the time that could have been spent travelling to diagnostic centers. This paper introduces a method for passive respiratory evaluation aimed at detecting pulmonary obstructive diseases by analyzing speech captured from individuals with asthma. The paper explores the various parameters from speech signal, hypothesizing the presence of traces of asthmatic impressions on the utterances.