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Related Experiment Video

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Emotion Recognizing by a Robotic Solution Initiative (EMOTIVE Project).

Grazia D'Onofrio1, Laura Fiorini2, Alessandra Sorrentino2

  • 1Clinical Psychology Service, Health Department, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, 71013 Foggia, Italy.

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|April 23, 2022
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Summary
This summary is machine-generated.

This study shows that the Random Forest (RF) algorithm accurately recognizes user emotions for social robots. The Pepper robot was found to be usable and acceptable, not hindering emotional responses.

Keywords:
acceptabilityhuman-robot interactionmonitoring of behaviorand internal states of humansnon-verbal cues and expressiveness

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Area of Science:

  • Human-Robot Interaction
  • Affective Computing
  • Machine Learning Applications

Background:

  • Emotion recognition is crucial for social robots, requiring efficient algorithms for real-time data processing.
  • Investigating traditional machine learning for individual emotion assessment and robotic modality comparison is essential.

Purpose of the Study:

  • To assess user emotions separately using machine learning algorithms.
  • To compare emotion recognition between static and motion robotic modalities.
  • To evaluate end-user acceptability and usability of assistive robots.

Main Methods:

  • Participants viewed images from the International Affective Picture System (IAPS) while interacting with the Pepper robot.
  • Emotion recognition was evaluated using k-nearest neighbors (KNN) and Random Forest (RF) algorithms on video recordings.
  • User acceptability and usability were assessed via the Almere Model Questionnaire (AMQ) and System Usability Scale (SUS).

Main Results:

  • The Random Forest (RF) algorithm demonstrated superior accuracy (0.98) and execution time compared to the k-nearest neighbors (KNN) algorithm.
  • RF achieved high precision and F-measure (0.98) across neutral, positive, and negative emotions.
  • Participants reported high usability and acceptability of the robotic solution.

Conclusions:

  • The Random Forest algorithm is more effective for emotion recognition in social robots than KNN.
  • The employed robotic system did not negatively impact users' emotional responses.