PULSE: Privileged Knowledge Transfer from Rich to Deployable Sensors for Embodied Multi-Sensory Learning

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Internet of Things (IoT) & Connected Devices, Health & Medical Research · Depth: Advanced, extended

Summary

PULSE is a novel framework designed for stress detection that leverages Electrodermal Activity (EDA) as "privileged information" during training, while enabling inference using only low-cost sensors like ECG, BVP, ACC, and TEMP. The framework addresses the challenge that EDA, a strong indicator for stress, requires expensive hardware often absent in commercial wearables. PULSE employs a self-supervised pretraining stage where encoder outputs are separated into shared and private embeddings. Shared embeddings are aligned across modalities and fused into a modality-invariant representation, while private embeddings retain modality-specific information for reconstruction. Subsequently, a frozen EDA teacher transfers sympathetic-arousal representations to student encoders, which then operate without EDA at inference. Evaluated on the WESAD dataset, PULSE demonstrates strong stress-detection performance, even outperforming models that use all sensors, including EDA, at test time, by effectively transferring EDA's diagnostic power to more accessible physiological signals.

Key takeaway

For AI Scientists and Machine Learning Engineers developing wearable stress monitoring solutions, PULSE offers a compelling approach to improve model accuracy while reducing hardware costs. By using EDA as a privileged teacher during training, your models can achieve superior stress detection performance with only readily available, low-cost sensors (ECG, BVP, ACC, TEMP) at inference. Consider adopting this knowledge transfer paradigm to enhance the robustness and generalizability of your models, especially in scenarios where high-fidelity EDA sensors are impractical for deployment.

Key insights

PULSE transfers privileged EDA knowledge to low-cost sensors, improving stress detection without requiring EDA at inference.

Principles

• Separate shared and private embeddings.
• Align shared embeddings across modalities.
• Use a frozen teacher for stable knowledge transfer.

Method

PULSE involves self-supervised pretraining with shared/private embedding separation and alignment, followed by knowledge transfer from a frozen EDA teacher to student encoders for low-cost sensors.

In practice

• Utilize EDA during training to enhance low-cost sensor performance.
• Implement hinge loss for cross-modal shared embedding alignment.
• Apply reconstruction loss to prevent representational collapse.

Topics

• Privileged Knowledge Transfer
• Electrodermal Activity
• Stress Monitoring
• Multimodal Physiological Signals
• Wearable Computing

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.