# AI-enabled Sound Pattern Recognition on Asthma Medication Adherence: Evaluation with the RDA Benchmark Suite

arXiv: https://arxiv.org/abs/2205.15360 · HTML: https://mlsanity.com/paper/2205.15360/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 134 references that are in the map.
- First posted on arXiv 30 May 2022.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks](https://mlsanity.com/paper/1901.10323/) (2019)
- [Max-Pooling Dropout for Regularization of Convolutional Neural Networks](https://mlsanity.com/paper/1512.01400/) (2015)
- [Deep Learning Framework Applied for Predicting Anomaly of Respiratory Sounds](https://mlsanity.com/paper/2012.13668/) (2020)
- [SwishNet: A Fast Convolutional Neural Network for Speech, Music and Noise Classification and Segmentation](https://mlsanity.com/paper/1812.00149/) (2018)
- [A Review of Audio Features and Statistical Models Exploited for Voice Pattern Design](https://mlsanity.com/paper/1502.06811/) (2015)

## Built on by (most cited first, 1 of 1)

- [Respiratory Inhaler Sound Event Classification Using Self-Supervised Learning](https://mlsanity.com/paper/2504.11246/) (2025)

## Abstract (arXiv)

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.