Human-Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning.

ABSTRACT: With the development of science and technology, human-machine interaction has brought great benefits to the society. Here, we design a voice and gesture signal translator (VGST), which can translate natural actions into electrical signals and realize efficient communication in human-machine interface. By spraying silk protein on the copper of the device, the VGST can achieve improved output and a wide frequency response of 20-2000 Hz with a high sensitivity of 167 mV/dB, and the resolution of frequency detection can reach 0.1 Hz. By designing its internal structure, its resonant frequency and output voltage can be adjusted. The VGST can be used as a high-fidelity platform to effectively recover recorded music and can also be combined with machine learning algorithms to realize the function

SUBMITTER: Luo H 

PROVIDER: S-EPMC10080540 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature