Sign-to-speech translation using machine-learning-assisted stretchable sensor arrays

By Zhihao Zhou, Kyle J Chen, Xiaoshi Li, Songlin Zhang, Yufen Wu, Yihao Zhou, Keyu Meng, Chenchen Sun, Qiang He, Wenjing Fan and 6 more. Nature Electronics, 2020

737 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/668fdb1df55ecfa8e97cb3fb6acb1275aa5b4b3b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Chengkuo Lee
2. Feng Wen
3. Tianyiyi He
4. Zixuan Zhang
5. Xuecheng Qu
6. Danling Luo
7. Zhou Li
8. Chan Wang

In Cognitive Neuroscience

1. Chengkuo Lee
2. Feng Wen
3. Tianyiyi He
4. Zixuan Zhang
5. Zhou Li
6. Xinge Guo
7. Long Liu

In Human-Computer Interaction

1. Chengkuo Lee

Citations in the corpus

• AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove (2021, methodology): “PCA employs the eigenvalues and eigenvectors of the data-matrix, which can point along the major variation directions of data to achieve the purpose of dimension reduction.”
• Artificial tactile perception smart finger for material identification based on triboelectric sensing (2022, positive): “Integrating machine learning enables material recognition accuracy up to 96.8% Machine learning has been shown to be an effective tool for automatically extracting features from the time-domain data of tribo-electric signals and for high-precision identification of different samples ( 50 ).”
• Design, manufacturing and applications of wearable triboelectric nanogenerators (2021, neutral): “Under the premise of not significantly sacrificing system performance, effective encapsulation could endow the system with weatherability and especially resist the erosion of sensitive components by sweat [118,212].”