SeNic: An Open Source Dataset for sEMG-Based Gesture Recognition in Non-Ideal Conditions

SeNic: An Open Source Dataset for sEMG-Based Gesture Recognition in Non-Ideal Conditions

In order to reduce the gap between the laboratory environment and actual use in daily life of human-machine interaction based on surface electromyogram (sEMG) intent recognition, this paper presents a benchmark dataset of sEMG in non-ideal conditions (<italic>SeNic</italic>). The dataset...

Full description

Bibliographic Details
Main Authors: Bo Zhu, Daohui Zhang, Yaqi Chu, Yalun Gu, Xingang Zhao
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Surface electromyogram (sEMG)
benchmark dataset
gesture recognition
non-ideal conditions
Online Access:https://ieeexplore.ieee.org/document/9771219/

Internet

https://ieeexplore.ieee.org/document/9771219/

Similar Items