TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning
This work addresses the challenge of developing domain-agnostic intelligent systems for analyzing physiological data, though it is incremental as it adapts existing self-supervised and transformer methods to this specific domain.
The paper tackles the problem of limited labeled physiological data by proposing a self-supervised learning framework using momentum contrast to learn representations from multivariate time-series without labels, showing it can learn discriminative features for downstream classification tasks on human activity and emotion recognition datasets.
Limited availability of labeled physiological data often prohibits the use of powerful supervised deep learning models in the biomedical machine intelligence domain. We approach this problem and propose a novel encoding framework that relies on self-supervised learning with momentum contrast to learn representations from multivariate time-series of various physiological domains without needing labels. Our model uses a transformer architecture that can be easily adapted to classification problems by optimizing a linear output classification layer. We experimentally evaluate our framework using two publicly available physiological datasets from different domains, i.e., human activity recognition from embedded inertial sensory and emotion recognition from electroencephalography. We show that our self-supervised learning approach can indeed learn discriminative features which can be exploited in downstream classification tasks. Our work enables the development of domain-agnostic intelligent systems that can effectively analyze multivariate time-series data from physiological domains.