Due to the individual heterogeneities among human subjects, researchers observed performance gaps 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 favored due to the factors such as the new-user-adaptation issue, system complexities, etc. To improve the performance of the generic model, we propose a representation learning framework that learns participant-invariant, named PiRL. The proposed framework constrains the latent space using maximum mean discrepancy (MMD) to close the distribution gap among subjects. Further, a triplet loss is utilized to optimize the learned representations for downstream health applications. We evaluate our frameworks on two public datasets for human physical and mental health problems detecting sleep apnea and stress, respectively. As preliminary results, we found the proposed approach shows around a 5\% increase in accuracy with statistical differences compared to baseline.