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Human-robot collaboration (HRC) in industrial settings increases productivity but introduces ergonomic and psychological stress risks. This paper presents a hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model integrating fuzzy logic and artificial neural networks (ANN) to evaluate ergonomic risk and stress in HRC. The model processes imprecise human data (e.g., posture, fatigue indicators, cognitive load) and uses the ANN to learn complex relationships between task factors, particularly in stress-inducing scenarios. An experimental study utilizing a UR10e collaborative robot and RGB-D camera compare manual and collaborative task phases, measuring stress levels through NASA-RTLX and Visual Analogue Scale (VAS). The model’s performance is validated against traditional methods like RULA. This approach offers a robust tool for real-time risk assessment in HRC workstations, advancing worker-centric design and adaptive HRC systems.

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Human-Robot Collaboration: An ANFIS-Based Model for Ergonomic Risk and Stress Evaluation

  • 会议论文

  • 发表日期：

    2025年

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  • Emine Bozkus,
  • Ella-Mae Hubbard,
  • Claire Guo

摘要

Human-robot collaboration (HRC) in industrial settings increases productivity but introduces ergonomic and psychological stress risks. This paper presents a hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model integrating fuzzy logic and artificial neural networks (ANN) to evaluate ergonomic risk and stress in HRC. The model processes imprecise human data (e.g., posture, fatigue indicators, cognitive load) and uses the ANN to learn complex relationships between task factors, particularly in stress-inducing scenarios. An experimental study utilizing a UR10e collaborative robot and RGB-D camera compare manual and collaborative task phases, measuring stress levels through NASA-RTLX and Visual Analogue Scale (VAS). The model’s performance is validated against traditional methods like RULA. This approach offers a robust tool for real-time risk assessment in HRC workstations, advancing worker-centric design and adaptive HRC systems.