  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
(https://gitlab.com/vvr/monitoring-medication-adherence/rda-benchmark) 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.
