Content area
Full Text
http://crossmark.crossref.org/dialog/?doi=10.1186/10.1007/s10916-016-0642-y-x&domain=pdf
Web End = http://crossmark.crossref.org/dialog/?doi=10.1186/10.1007/s10916-016-0642-y-x&domain=pdf
Web End = J Med Syst (2016) 40: 285DOI 10.1007/s10916-016-0642-y
MOBILE & WIRELESS HEALTH
Energy Efficient Monitoring of Metered Dose Inhaler Usage
Aris S. Lalos1 John Lakoumentas1 Anastasios Dimas1 Konstantinos Moustakas1
http://orcid.org/0000-0003-0511-9302
Web End = Received: 26 February 2016 / Accepted: 17 October 2016 / Published online: 29 October 2016 Springer Science+Business Media New York 2016
Abstract Life-long chronic inflammatory diseases of the airways, such as asthma and Chronic Obstructive Pulmonary Disease, are very common worldwide, affecting people of all ages, race and gender. One of the most important aspects for the effective management of asthma is medication adherence which is defined as the extent to which patients follow their prescribed action plan and use their inhaler correctly. Wireless telemonitoring of the medication adherence can facilitate early diagnosis and management of these diseases through the use of an accurate and energy efficient mHealth system. Therefore, low complexity audio compression schemes need to be integrated with high accuracy classification approaches for the assessment of adherence of patients that use of pressurized Metered Dose Inhalers (pMDIs). To this end, we propose a novel solution that enables the energy efficient monitoring of metered dose inhaler usage, by exploiting the specific characteristics of the reconstructed audio features at the receiver. Simulation studies, carried out with a large dataset of indoor & outdoor measurements have led to high levels of accuracy (98 %) utilizing only 2 % of the recorded audio samples at the receiver, demonstrating the potential of this method for the development of novel energy efficient inhalers and medical devices in the area of respiratory medicine.
This article is part of the Topical Collection on Mobile & Wireless Health
[envelopeback] Aris S. Lalos mailto:[email protected]
Web End [email protected]
1 Electrical & Computer Engineering Department, University of Patras, Patras, Greece
Keywords Compressed sensing AdaBoost
Time-frequency analysis Support vector machines
Introduction
Asthma is a chronic disease of the airways that affects more than 235 million people worldwide [1]. In the region of Europe, 30 million adults suffer from asthma [2], while the number of children suffering from the disease is continuously rising [3]. This diversity of asthma prevalence is a global phenomenon [4] and reveals the inability of even developed countries to effectively support asthma patients...