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Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications.
Aitor Toichoa Eyam1, Wael M Mohammed1, Jose L Martinez Lastra1
1FAST-Lab, Faculty of Engineering and Natural Sciences, Tampere University, 3320 Tampere, Finland.
Sensors (Basel, Switzerland)
|July 24, 2021
Summary
This study introduces a novel approach for collaborative robots (Cobots) to adapt their parameters based on human emotional states, detected via Electroencephalography (EEG). This enhances human-robot trust and collaboration in Industry 4.0 environments.
Area of Science:
- Robotics
- Human-Computer Interaction
- Affective Computing
Background:
- Robotic systems and Industry 4.0 adoption are rapidly increasing.
- Collaborative robots (Cobots) enable human-robot physical interaction but lack emotional state recognition.
- Keeping humans in the loop is crucial for increasing engagement rather than replacement.
Purpose of the Study:
- To present an approach for adapting cobot parameters to the human worker's emotional state.
- To enhance trust and confidence in human-cobot interactions.
- To review technologies for emotional sensing and recognition.
Main Methods:
- Utilizing Electroencephalography (EEG) to digitize and understand human emotional states.
- Instantly adjusting cobot parameters based on detected emotional states.
- Testing the approach on an ABB YuMi cobot with an EEG headset.
Main Results:
- Demonstrated a method for real-time adaptation of cobot parameters to human emotions.
- Showcased the potential for increased human-cobot trust and collaboration.
- Provided a review of current emotional sensing and recognition technologies.
Conclusions:
- Adapting cobot behavior to human emotional states is feasible using EEG.
- This approach can significantly improve the safety and effectiveness of human-robot collaboration.
- Further research can advance affective computing in industrial robotics.
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