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 mainly cons...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής

Κύριοι συγγραφείς:	Bo Zhu, Daohui Zhang, Yaqi Chu, Yalun Gu, Xingang Zhao
Μορφή:	Artigo
Γλώσσα:	Inglês
Έκδοση:	IEEE 2022-01-01
Σειρά:	IEEE Transactions on Neural Systems and Rehabilitation Engineering
Θέματα:	Surface electromyogram (sEMG) benchmark dataset gesture recognition non-ideal conditions
Διαθέσιμο Online:	https://ieeexplore.ieee.org/document/9771219/
Ετικέτες:	Προσθήκη ετικέτας Δεν υπάρχουν, Καταχωρήστε ετικέτα πρώτοι!