用联邦学习实现工业机器人对工人心理状态的隐私保护个性化评估
Personalized Mental State Evaluation in Human-Robot Interaction using Federated Learning
- 通过联邦学习在本地设备训练，不共享原始生理数据
- 多模态生理信号预测准确率接近中心化训练
- 适合关注员工心理健康与数据隐私的智能制造场景
随着工业5.0的到来，制造商越来越重视员工福祉与个性化生产。压力感知的人机协作（HRC）在此范式中至关重要，要求机器人能根据人类心理状态调整行为以提升协作流畅性与安全性。本文提出一种融合联邦学习（FL）的新框架，实现个性化心理状态评估同时保护用户隐私。利用脑电（EEG）、心电（ECG）、皮电（EDA）、肌电（EMG）和呼吸信号等多模态生理数据，构建模型实时预测操作员压力水平，支持机器人动态适应。基于联邦学习的方法实现分布式本地训练，确保数据保密性，同时提升模型泛化能力与个体定制化水平。实验表明，该框架在全局模型上的压力预测准确率与集中式训练相当；且显著增强个性化能力，优化工业环境中人机交互效果，同时保障数据隐私。该方法推动了隐私保护型自适应机器人发展，助力智能制造业员工福祉提升。
原文摘要 · Abstract (English)
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.
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