SpiroActive: Active Learning for Efficient Data Acquisition for Spirometry

TL;DR

This paper introduces SpiroActive, an active learning approach to reduce data collection costs in wearable spirometry by strategically selecting samples, enabling effective model training with less data.

Contribution

It applies active learning to wearable spirometry data, demonstrating efficient data acquisition and comparable or improved model performance with fewer labeled samples.

Findings

Active learning reduces data collection costs.

Models trained on selected samples perform as well or better.

Efficient data sampling improves wearable spirometry models.

Abstract

Respiratory illnesses are a significant global health burden. Respiratory illnesses, primarily Chronic obstructive pulmonary disease (COPD), is the seventh leading cause of poor health worldwide and the third leading cause of death worldwide, causing 3.23 million deaths in 2019, necessitating early identification and diagnosis for effective mitigation. Among the diagnostic tools employed, spirometry plays a crucial role in detecting respiratory abnormalities. However, conventional clinical spirometry methods often entail considerable costs and practical limitations like the need for specialized equipment, trained personnel, and a dedicated clinical setting, making them less accessible. To address these challenges, wearable spirometry technologies have emerged as promising alternatives, offering accurate, cost-effective, and convenient solutions. The development of machine learning models for wearable spirometry heavily relies on the availability of high-quality ground truth spirometry data, which is a laborious and expensive endeavor. In this research, we propose using active learning, a sub-field of machine learning, to mitigate the challenges associated with data collection and labeling. By strategically selecting samples from the ground truth spirometer, we can mitigate the need for resource-intensive data collection. We present evidence that models trained on small subsets obtained through active learning achieve comparable/better results than models trained on the complete dataset.