Home // AMBIENT 2019, The Ninth International Conference on Ambient Computing, Applications, Services and Technologies // View article
Authors:
Md Juber Rahman
Ebrahim Nemati
Md Mahbubur Rahman
Korosh Vatanparvar
Viswam Nathan
Jilong Kuang
Keywords: cough; online detection; pulmonary disease; random forest; streaming audio
Abstract:
An automated monitoring of chronic diseases may help in the early identification of exacerbation, reduction of healthcare expenditure, as well as improve patient's health-related quality of life. Cough monitoring provides valuable information in the assessment of asthma and Chronic Obstructive Pulmonary Disease (COPD). In this multi-cohort study, we have used every-day wearables such as smartphone and smartwatch to collect cough instances from 131 subjects including 69 asthma patients, 9 COPD patients, 13 patients with a co-morbidity of asthma and COPD and 40 healthy controls. For online cough detection we have identified the audio features suitable for resource-constrained platforms (e.g., smartphone), ranked the features and identified top 9 features to obtain an F-1 score of 99.8% in the offline classification of 23,884 cough instances from non-cough (speech/silence, etc.) events using Random Forest classifier. Finally, a power and time-efficient scheme for continuous online cough detection from the audio stream has been illustrated. The proposed model has an online cough detection sensitivity of 93.3%, specificity of 98.8% and accuracy of 98.8%. In addition, a good improvement in reducing the on-device execution (feature extraction and classification) time and power consumption has been achieved compared to the current state of the art algorithms. The proposed on-device cough detector has been implemented to meet the criteria for integration in the passive monitoring and online assessment of asthma/COPD patients.
Pages: 1 to 7
Copyright: Copyright (c) IARIA, 2019
Publication date: September 22, 2019
Published in: conference
ISSN: 2326-9324
ISBN: 978-1-61208-739-9
Location: Porto, Portugal
Dates: from September 22, 2019 to September 26, 2019