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This study introduces an interactive data glove system that recognizes emotions and hand gestures from physiological signals. This enables simultaneous control of virtual hands and emotion-driven manipulators.

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Area of Science:

  • Human-Computer Interaction
  • Affective Computing
  • Robotics

Background:

  • Physiological signals offer a rich source of data for understanding human emotional and intentional states.
  • Integrating emotion recognition with physical interfaces like data gloves presents opportunities for intuitive control systems.

Purpose of the Study:

  • To investigate the interactive application of data gloves integrated with an emotion recognition and judgment system.
  • To develop a system capable of recognizing hand posture and emotion for controlling virtual and physical devices.

Main Methods:

  • Established an emotion recognition and judgment system using optimal physiological signal features.
  • Constructed a multi-channel data-transmitting data glove recognizing hand posture and emotion.
  • Built a virtual hand control system and an emotion-driven manipulator.

Main Results:

  • The data glove successfully enabled simultaneous control of a virtual hand and a manipulator.
  • The system could control virtual hand gestures directly from physiological signals, independent of hand movements.
  • Emotion-driven manipulator control was successful for two specific emotional trends.

Conclusions:

  • Data gloves combined with emotion recognition provide a novel method for simultaneous gesture and emotion-based control.
  • The developed system demonstrates potential for intuitive human-robot interaction and affective interfaces.
  • Further research is needed to refine emotion-driven control for a wider range of emotional states.