PPG Sensing for Human-Centered Interaction

An end-to-end system that captures PPG via remote video and contact sensors, processes signals in real time, and translates physiological change into interactive feedback.

Research + Interaction Design
End-to-End System

Overview

This project explores how photoplethysmography (PPG) from remote video (rPPG) and contact sensors can become meaningful signals for interaction, identity, and health-focused systems.

End-to-End Pipeline

Motivation

Physiological signals are rich but often treated as backend analytics. This work asks how PPG can power real-time, human-centered interactions in the wild.

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How can PPG enable responsive interaction?

We focus on multimodal sensing and robust processing so interaction logic can respond to state changes, not just static biometrics.

System Architecture

Three coordinated layers connect sensing to signal processing to interaction.

Sensing Layer Remote rPPG, contact PPG, and optional auxiliary sensors.
Signal Processing Layer Filtering, feature estimation, and cross-modality alignment.
Interaction Layer State-aware feedback and responsive system behavior.

Architecture

Sensing Capture PPG signals from video and wearable sensors.
Processing Extract stable pulse features with robust filtering.
Interaction Translate signals into adaptive, human-centered feedback.

Signal Processing Pipeline

From face-based ROI extraction to filtered signals and validated features.

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Interaction Design

Physiological change becomes state-aware feedback and implicit interaction triggers.

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Evaluation & Insights

rPPG is sensitive to motion and lighting, but multimodal context improves stability and user understanding.

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Contribution

An end-to-end PPG sensing and interaction system with a modular pipeline and design insights for real-time, state-aware feedback.

"A full pipeline from sensing through interaction makes PPG usable in real time."

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System Insight
End-to-End Flow

"Multimodal synchronization is essential for reliable signal interpretation."

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System Insight
Cross-Modal Alignment

"Users respond to relative physiological change more intuitively than raw values."

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Design Insight
State-Aware Feedback

Future Directions

Scaling toward deployment, authentication, and longitudinal sensing.

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Real-World Deployment

Evaluate stability across varied lighting, motion, and environments.

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Physiological identity

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Signal quality assessment

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Multi-user sensing