Disentangled Adversarial Transfer Learning for Physiological Biosignals.

Area of Science:

• Biomedical Engineering
• Machine Learning
• Wearable Technology

Background:

• Wearable sensors offer convenient physiological monitoring.
• Transfer learning for biosignal analysis faces challenges due to domain inconsistency across users and sessions.
• Accurate physiological status assessment requires robust models adaptable to diverse data.

Purpose of the Study:

• To propose an adversarial inference approach for transfer learning in physiological biosignal analysis.
• To extract disentangled, nuisance-robust representations for stress status level assessment.
• To improve the adaptability of biosignal models to different users and recording conditions.

Main Methods:

• Developed an adversarial inference framework utilizing an encoder, adversary network, and nuisance network.
• Exploited the trade-off between task-related and person-discriminative features.
• Employed a discriminative classifier on disentangled latent representations.

Main Results:

• Demonstrated significant benefits of the adversarial framework in cross-subjects transfer evaluations.
• Showcased the model's capability to adapt to a broader range of subjects.
• Validated the effectiveness of disentangled representation learning for stress assessment.

Conclusions:

• The proposed adversarial transfer learning approach effectively addresses domain inconsistency in biosignal analysis.
• This method enhances the robustness and generalizability of physiological status monitoring systems.
• The framework is adaptable to other deep feature learning applications in biosignal processing.