  Due to individual heterogeneity, performance gaps are observed between
generic (one-size-fits-all) models and person-specific models in data-driven
health applications. However, in real-world applications, generic models are
usually more favorable due to new-user-adaptation issues and system
complexities, etc. To improve the performance of the generic model, we propose
a representation learning framework that learns participant-invariant
representations, named PiRL. The proposed framework utilizes maximum mean
discrepancy (MMD) loss and domain-adversarial training to encourage the model
to learn participant-invariant representations. Further, a triplet loss, which
constrains the model for inter-class alignment of the representations, is
utilized to optimize the learned representations for downstream health
applications. We evaluated our frameworks on two public datasets related to
physical and mental health, for detecting sleep apnea and stress, respectively.
As preliminary results, we found the proposed approach shows around a 5%
increase in accuracy compared to the baseline.
