Spectral Collaborative Representation based Classification for Hand Gestures recognition on Electromyography Signals

by   Ali Boyali, et al.

In this study, we introduce a novel variant and application of the Collaborative Representation based Classification in spectral domain for recognition of the hand gestures using the raw surface Electromyography signals. The intuitive use of spectral features are explained via circulant matrices. The proposed Spectral Collaborative Representation based Classification (SCRC) is able to recognize gestures with higher levels of accuracy for a fairly rich gesture set. The worst recognition result which is the best in the literature is obtained as 97.3% among the four sets of the experiments for each hand gestures. The recognition results are reported with a substantial number of experiments and labeling computation.


page 7

page 8

page 10

page 13

page 14

page 16

page 17

page 18


Selecting a Small Set of Optimal Gestures from an Extensive Lexicon

Finding the best set of gestures to use for a given computer recognition...

putEMG -- a surface electromyography hand gesture recognition dataset

In this paper, we present a putEMG dataset intended for evaluation of ha...

Detection of bimanual gestures everywhere: why it matters, what we need and what is missing

Bimanual gestures are of the utmost importance for the study of motor co...

Quantifying the Security of Recognition Passwords: Gestures and Signatures

Gesture and signature passwords are two-dimensional figures created by d...

Online Recognition of Incomplete Gesture Data to Interface Collaborative Robots

Online recognition of gestures is critical for intuitive human-robot int...

Adaptive EMG-based hand gesture recognition using hyperdimensional computing

Accurate recognition of hand gestures is crucial to the functionality of...

Please sign up or login with your details

Forgot password? Click here to reset