Self-Supervised Contrastive Learning for Remote Detection of Early Parkinson’s Disease by Mobile Phone Digital Biomarkers

Le résumé fourni par la source

As a ubiquitous portable device, mobile phones play an important role in large-scale data collection and remote health detection. Parkinson's disease (PD), a typical movement disorder, can be detected by capturing digital biomarkers using mobile phone sensors. Nevertheless, it is difficult to obtain reliable label information in large-scale remote data collection, especially for time-series digital biomarkers. Based on this, we develop a novel multi-dimensional self-supervised contrastive learning framework for remote detection of early PD by mobile phone time-series digital biomarkers. Specifically, depending on two different augmentation views, the proposed framework considers temporal contrasting, spatial contrasting, contextual contrasting, and inter-modal contrasting to enable the model to learn more discriminative features. For temporal and spatial contrasting, certain time steps (channels) of one view are used to predict the next time steps (channels) of the other view, thereby constructing a cross-view prediction task. Meanwhile, contextual contrasting is introduced into the contrast framework to consider temporal prediction and spatial prediction context representation, respectively, further increasing similarity between positive pairs and decreasing it between negative pairs. In addition, inter-modal contrasting is used to force the model to capture the potential relationship between different modal data. Experimental results show that fine-tuning with only 10% of the labeled data can outperform supervised learning and generally surpass state-of the-art algorithms.

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