Digital Health Sensor Data in Autism: Developing Few Shot Learning Approaches for Traditional Machine Learning Classifiers

Abstract

There is great interest in applying artificial intelligence (AI) techniques to healthcare issues such as Autism, particularly in combination with digital health technologies (robots, wearables, smartphones, etc.) in user homes. However, a critical challenge is that modern AI techniques like deep learning (DL) typically require large datasets with millions of samples, yet in healthcare we are often working with smaller clinical samples (<50 participants). To address that challenge, we need to develop new approaches that can learn more efficiently from less data. In this paper, we propose a novel approach to few-shot learning (FSL) called SMOTE_FSL, which is applicable to traditional machine learning (ML) models, allowing them to work with smaller sample sizes as well as various types of healthcare data (not only image or text data). We compare SMOTE_FSL on two healthcare sensor datasets gathered using robots and wearables, with results showing SMOTE_FSL performs comparably to state-of-the-art DL-based FSL methods (e.g. autoencoders, generative adversarial networks [GAN]). That indicates such an approach holds potential to expand utilization of FSL to a broad range of healthcare data derived from smaller clinical sample sizes.