Noncontact Human-Machine Interface Using Complementary Information Fusion Based on MEMS and Triboelectric Sensors

Area of Science:

• Materials Science
• Sensor Technology
• Human-Computer Interaction

Background:

• Existing noncontact human-machine interfaces (HMIs) often exhibit high power consumption and limited interaction capabilities.
• There is a need for minimalist, low-power, and multidimensional noncontact interaction solutions.

Purpose of the Study:

• To develop a novel, low-power, multimodal noncontact interaction interface.
• To fuse data from a microelectromechanical system (MEMS) humidity sensor and a triboelectric sensor for enhanced functionality.

Main Methods:

• Utilized a MEMS humidity sensor based on an aluminum nitride (AlN) bulk wave resonator and graphene oxide (GO) film.
• Integrated a facile triboelectric sensor with annular aluminum electrodes.
• Leveraged resonant frequency changes and output voltage waveforms for interaction detection.

Main Results:

• Achieved an ultra-tiny form factor (200 × 400 µm²) humidity sensor with high signal strength (Q = 1729.5) and low noise (±0.31%RH).
• Demonstrated continuous and steady interaction with an approaching finger via the humidity sensor.
• Enabled rapid recognition of multidirectional finger movements using the triboelectric sensor.

Conclusions:

• The proposed interface successfully functions as a game control for virtual reality (VR) and a password input system.
• This technology shows significant potential for diverse applications in the future Metaverse.
• The fusion of humidity and triboelectric sensing offers a promising approach for advanced noncontact HMIs.