DeepAI AI Chat
Log In Sign Up

Statistical Analysis of Time-Frequency Features Based On Multivariate Synchrosqueezing Transform for Hand Gesture Classification

by   Lutfiye Saripinar, et al.

In this study, the four joint time-frequency (TF) moments; mean, variance, skewness, and kurtosis of TF matrix obtained from Multivariate Synchrosqueezing Transform (MSST) are proposed as features for hand gesture recognition. A publicly available dataset containing surface EMG (sEMG) signals of 40 subjects performing 10 hand gestures, was used. The distinguishing power of the feature variables for the tested gestures was evaluated according to their p values obtained from the Kruskal-Wallis (KW) test. It is concluded that the mean, variance, skewness, and kurtosis of TF matrices can be candidate feature sets for the recognition of hand gestures.


page 1

page 3


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

In this study, we introduce a novel variant and application of the Colla...

putEMG -- a surface electromyography hand gesture recognition dataset

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

Hand Gesture Recognition Based on Karhunen-Loeve Transform

In this paper, we have proposed a system based on K-L Transform to recog...

The Impact of Quantity of Training Data on Recognition of Eating Gestures

This paper considers the problem of recognizing eating gestures by track...

Learning to recognize touch gestures: recurrent vs. convolutional features and dynamic sampling

We propose a fully automatic method for learning gestures on big touch d...

GestureKeeper: Gesture Recognition for Controlling Devices in IoT Environments

This paper introduces and evaluates the GestureKeeper, a robust hand-ges...

Force myography benchmark data for hand gesture recognition and transfer learning

Force myography has recently gained increasing attention for hand gestur...