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.

翻译：哮喘是一种常见、通常为长期性的呼吸系统疾病，对全球社会和经济产生负面影响。治疗涉及使用医疗设备（吸入器）将药物输送到气道，其效果取决于吸入技术的精确性。临床上需要在就诊期间评估吸入技术的客观方法。配备传感器的集成健康监测系统，通过智能结构嵌入的声音信号检测、分析和识别，能够实现药物触发的识别，从而为可靠的内容管理提供有力工具。配备传感器、嵌入声音信号检测的健康监测系统能够识别药物触发，并可用于有效的音频内容分析。本文重新审视了利用机器学习技术进行哮喘用药依从性评估的声音模式识别，并提出了呼吸与药物触发（RDA）套件（https://gitlab.com/vvr/monitoring-medication-adherence/rda-benchmark），用于基准测试和进一步研究。RDA套件包含一套用于音频处理、特征提取和分类流程的工具，并附带一个包含呼吸和药物触发声音的数据集。RDA中的分类模型基于传统和先进的机器学习及深度网络架构实现。本研究对已实现的方法进行了比较评估，探讨了潜在的改进，并讨论了挑战和未来趋势。