Personalized Mental State Evaluation in Human-Robot Interaction using Federated Learning
Journal:
arXiv
Published Date:
Jun 25, 2025
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.
