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Applications of Stress

Applications of Stress

Consider a structure made of a boom and a rod designed to support a load. These two components are connected by a pin and stabilized by brackets and pins. The boom and the rod are detached from their supports to assess the different stresses imposed on this structure, and a free-body diagram is drawn. Then, all the forces applied, including the load acting on the structure, are identified. The reaction forces exerted on both the boom and the rod are computed using the equilibrium equations.
The...

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Assessing operator stress in collaborative robotics: A multimodal approach.

Simone Borghi¹, Andrea Ruo², Lorenzo Sabattini²

¹Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Modena, Italy.

Applied Ergonomics

|November 17, 2024

Summary

Human operators

Keywords:

Cobot programming Human monitoring Human–Robot Collaboration Psycho-physiological signals Stress evaluation Wearable sensors

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Assessment of Stress Effects on Cognitive Flexibility using an Operant Strategy Shifting Paradigm

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Area of Science:

Human-Robot Collaboration (HRC)
Industry 4.0
Manufacturing Automation

Background:

Operator stress and discomfort are significant barriers to Human-Robot Collaboration (HRC) adoption in Industry 4.0 manufacturing.
Neglecting human factors, including psycho-physiological responses, hinders efficient cobot implementation.
Understanding operator stress is crucial for successful human-robot integration.

Purpose of the Study:

To investigate the relationship between physiological signals and subjective stress perception during cobot programming tasks.
To identify reliable physiological indicators of operator stress in collaborative robot environments.
To assess the accuracy of various physiological parameters in reflecting operator stress levels.

Main Methods:

Volunteers performed cobot programming tasks while physiological data (EEG, ECG, GSR, facial expressions) were recorded.
Subjective stress was assessed using the NASA-TLX questionnaire.
Correlation analysis was performed between physiological parameters and subjective stress scores.

Main Results:

Electroencephalogram (EEG) Theta, Alpha, and Beta power, along with Galvanic Skin Response (GSR) recovery and rise times, showed high alignment with subjective stress.
Heart Rate Variability (HRV) metrics (PNN25, SDNN, PNN50, RMSSD) also demonstrated significant correlation with perceived stress.
Raw RR interval data and recorded emotions showed lower accuracy in predicting subjective stress.

Conclusions:

Specific EEG, GSR, and HRV parameters are reliable physiological indicators of operator stress during HRC tasks.
These findings can inform the development of stress-monitoring systems for safer and more efficient human-robot collaboration.
Prioritizing human factors through physiological monitoring is key to successful automation implementation.