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Due to individual heterogeneity, person-specific models are usually achievingbetter performance than generic (one-size-fits-all) models in data-drivenhealth applications. However, generic models are usually preferable inreal-world applications, due to the difficulties of developing person-specificmodels, such as new-user-adaptation issues and system complexities. To improvethe performance of generic models, we propose a Participant-invariantRepresentation Learning (PiRL) framework, which utilizes maximum meandiscrepancy (MMD) loss and domain-adversarial training to encourage the modelto learn participant-invariant representations. Further, to avoid trivialsolutions in the learned representations, a triplet loss based constraint isused for the model to learn the label-distinguishable embeddings. The proposedframework is evaluated on two public datasets (CLAS and Apnea-ECG), andsignificant performance improvements are achieved compared to the baselinemodels.

PiRL: Participant-Invariant Representation Learning for Healthcare Using Maximum Mean Discrepancy and Triplet Loss