ArticleACS applied materials & interfaces2023
Human-Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning.
Article in ACS applied materials & interfaces, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A dual-track, physics-informed framework for predicting TENG open circuit voltage and short circuit current.iScience · 2026Article
- Advances in Biomaterials Development and Their Energy Harvesting Application.Research (Washington, D.C.) · 2026Review
- Recent Advances in Electrospun Nanofiber-Based Self-Powered Triboelectric Sensors for Contact and Non-Contact Sensing.Nanomaterials (Basel, Switzerland) · 2025Review
- Vocal performance evaluation of the intelligent note recognition method based on deep learning.Scientific reports · 2025Article
- Exploration of Advanced Applications of Triboelectric Nanogenerator-Based Self-Powered Sensors in the Era of Artificial Intelligence.Sensors (Basel, Switzerland) · 2025Review
- A sensor-actuator-coupled gustatory interface chemically connecting virtual and real environments for remote tasting.Science advances · 2025Article
- Research on the Performance of a Liquid-Solid Triboelectric Nanogenerator Prototype Based on Multiphase Liquid.Micromachines · 2025Article
- A Self-Powered, Skin Adhesive, and Flexible Human-Machine Interface Based on Triboelectric Nanogenerator.Nanomaterials (Basel, Switzerland) · 2024Article
- Gel-Based Triboelectric Nanogenerators for Flexible Sensing: Principles, Properties, and Applications.Nano-micro letters · 2024Review
- Flexible Self-Powered Low-Decibel Voice Recognition Mask.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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 of speech recognition with a high accuracy rate of 97%. It also has good antinoise performance to recognize speech correctly even in noisy environments. Meanwhile, in gesture recognition, the triboelectric translator is able to recognize simple hand gestures and to judge the distance between hand and the VGST based on the principle of electrostatic induction. This work demonstrates that triboelectric nanogenerator (TENG) technology can have great application prospects and significant advantages in human-machine interaction and high-fidelity platforms.
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