AI Sound Recognition on Asthma Medication Adherence: Evaluation With the RDA Benchmark Suite

Abstract

Asthma is a common, usually long-term respiratory disease with negative impact on global society and economy. Treatment involves using medical devices (inhalers) that distribute medication to the airways and its efficiency depends on the precision of the inhalation technique. There is a clinical need for objective methods to assess the inhalation technique, during clinical consultation. Integrated health monitoring systems, equipped with sensors, enable the recognition of drug actuation, embedded with sound signal detection, analysis and identification from intelligent structures, that could provide powerful tools for reliable content management. Health monitoring systems equipped with sensors, embedded with sound signal detection, enable the recognition of drug actuation and could be used for effective audio content analysis. This paper revisits sound pattern recognition with machine learning techniques for asthma medication adherence assessment and presents the Respiratory and Drug Actuation (RDA) Suite a for benchmarking and further research. The RDA Suite includes a set of tools for audio processing, feature extraction and classification procedures and is provided along with a dataset, consisting of respiratory and drug actuation sounds. The classification models in RDA are implemented based on conventional and advanced machine learning and deep networks' architectures. This study provides a comparative evaluation of the implemented approaches, examines potential improvements and discusses on challenges and future tendencies.

Current state and prospects of artificial intelligence in allergy

“…Another study applied ML to analyze the sounds of asthma inhalers to predict adequate usage and drug actuations. 149 Recorded sound on mobile devices has also been proposed to monitor lung function in asthmatics. 150 While requiring further validation, these techniques could be used to develop future telehealth solutions including smartphone-based applications, which have the potential to aid decision-making and self-monitoring in asthma.…”

“…This yielded accurate and real‐time cough count with a specificity of 92% and a specificity of 98%. Another study applied ML to analyze the sounds of asthma inhalers to predict adequate usage and drug actuations 149 . Recorded sound on mobile devices has also been proposed to monitor lung function in asthmatics 150 .…”