Gesture-controlled reconfigurable metasurface system based on surface electromyography for real-time electromagnetic...

Area of Science:

• Electromagnetics and Metamaterials
• Human-Machine Interaction (HMI)
• Signal Processing and Machine Learning

Background:

• Gesture recognition is crucial for advancing human-machine interaction (HMI).
• Current HMI systems require intuitive and precise control mechanisms.
• Metasurfaces offer dynamic control over electromagnetic waves but often lack real-time, user-driven modulation.

Purpose of the Study:

• To propose and demonstrate a novel gesture-controlled reconfigurable metasurface system.
• To enable real-time beam deflection and polarization conversion of electromagnetic waves.
• To integrate surface electromyography (sEMG) for intuitive gesture-based control of metasurfaces.

Main Methods:

• Development of a gesture recognition system utilizing surface electromyography (sEMG) signals.
• Implementation of a pre-trained convolutional neural network (CNN) model for sEMG signal interpretation.
• Dynamic modulation of a reconfigurable metasurface based on recognized user gestures for electromagnetic wave manipulation.

Main Results:

• Successful real-time recognition of user gestures through sEMG signal analysis.
• Demonstration of precise control over the deflection direction and polarization state of electromagnetic waves.
• High-precision electromagnetic wave manipulation achieved in response to various distinct gestures.

Conclusions:

• The proposed sEMG-based gesture-controlled metasurface system offers a powerful new paradigm for HMI.
• This technology enables intuitive and precise control of electromagnetic wave properties.
• Significant potential applications exist in intelligent device control, VR, and wireless communications, driving HMI innovation.