Frequency-selective acoustic and haptic smart skin for dual-mode dynamic/static human-machine interface

Area of Science:

• Materials Science
• Engineering
• Biomedical Engineering

Background:

• Accurate biosignal transmission is crucial for effective human-machine interfaces (HMIs).
• Environmental noise significantly interferes with biosignal acquisition and HMI performance.
• Existing sensors often struggle with noise immunity and dual-mode operation.

Purpose of the Study:

• To develop frequency-selective acoustic and haptic sensors for dual-mode HMIs.
• To achieve noise-independent voice recognition and tactile sensing capabilities.
• To enable advanced control of robotic systems via HMIs.

Main Methods:

• Fabrication of triboelectric sensors using ferroelectric composites with a hierarchical structure.
• Integration of multichannel acoustic sensor arrays.
• Utilizing artificial neural networks for signal processing and voice recognition.
• Testing sensor performance under various dynamic pressures and acoustic noise conditions.

Main Results:

• The developed sensors exhibit high sensitivity and linearity across a wide dynamic pressure range.
• Achieved high acoustic frequency selectivity from 145 to 9000 Hz.
• Demonstrated over 95% accurate voice recognition in the presence of noise (100 to 8000 Hz).
• Successfully differentiated surface textures and controlled an avatar robot using dual-mode inputs.

Conclusions:

• The proposed triboelectric sensors offer a robust solution for noise-independent HMIs.
• Dual-mode sensing capabilities enhance the functionality and applicability of HMIs.
• The technology facilitates advanced applications in robotics and human-computer interaction.