SALTS: Streamlined Adaptive Learning for Sensors Time Series

Abstract

Sensor-generated time series hold immense potential across the healthcare domain, yet present challenges in labelling due to their sequential nature, which requires consideration of context and temporal dependencies. Recognising the costly nature of data labelling and that domain experts may have limited technical expertise in model optimisation, we introduce an approach to automate machine learning model training for medical time series, enhancing analysis efficiency. Our proposal first operates at the data input level via adaptive data acquisition, facilitating the selection of highly-informative samples for labelling. Further, it works at the model level, through dynamic model refinement to optimise the model on-the-fly by progressively exploring the possible hyperparameter options and choosing the best combination at each acquisition step, and through an automatic learning phase to maximise the usage of any unlabelled samples. This results in a robust learning strategy that continuously refines the model with expanding data and human expertise. Demonstrated on EEG, ECG, and IMU health signal classification, our method outperforms baselines and the current state-of-the-art, while reducing reliance on human input for model tuning. SALTS enhances the applicability of machine learning to healthcare time series, maximising the information gained through each human annotation step in an automated way.