  Recent developments in wearable sensors demonstrate promising results for
monitoring physiological status in effective and comfortable ways. One major
challenge of physiological status assessment is the problem of transfer
learning caused by the domain inconsistency of biosignals across users or
different recording sessions from the same user. We propose an adversarial
inference approach for transfer learning to extract disentangled
nuisance-robust representations from physiological biosignal data in stress
status level assessment. We exploit the trade-off between task-related features
and person-discriminative information by using both an adversary network and a
nuisance network to jointly manipulate and disentangle the learned latent
representations by the encoder, which are then input to a discriminative
classifier. Results on cross-subjects transfer evaluations demonstrate the
benefits of the proposed adversarial framework, and thus show its capabilities
to adapt to a broader range of subjects. Finally we highlight that our proposed
adversarial transfer learning approach is also applicable to other deep feature
learning frameworks.
