Personalized Mental State Evaluation in Human-Robot Interaction using Federated Learning

TL;DR

This paper introduces a federated learning framework for personalized, privacy-preserving mental state evaluation in human-robot interaction, using multimodal physiological signals to improve robot adaptation and worker safety.

Contribution

It presents a novel federated learning-based system for real-time, personalized stress detection in industrial human-robot collaboration, maintaining data privacy and enhancing model generalization.

Findings

Federated learning achieves stress prediction accuracy comparable to centralized models.

Personalization through FL improves robot adaptation to individual stress levels.

The framework enhances worker safety and well-being in industrial settings.

Abstract

With the advent of Industry 5.0, manufacturers are increasingly prioritizing worker well-being alongside mass customization. Stress-aware Human-Robot Collaboration (HRC) plays a crucial role in this paradigm, where robots must adapt their behavior to human mental states to improve collaboration fluency and safety. This paper presents a novel framework that integrates Federated Learning (FL) to enable personalized mental state evaluation while preserving user privacy. By leveraging physiological signals, including EEG, ECG, EDA, EMG, and respiration, a multimodal model predicts an operator's stress level, facilitating real-time robot adaptation. The FL-based approach allows distributed on-device training, ensuring data confidentiality while improving model generalization and individual customization. Results demonstrate that the deployment of an FL approach results in a global model with performance in stress prediction accuracy comparable to a centralized training approach. Moreover, FL allows for enhancing personalization, thereby optimizing human-robot interaction in industrial settings, while preserving data privacy. The proposed framework advances privacy-preserving, adaptive robotics to enhance workforce well-being in smart manufacturing.