Work

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

Year: 2022

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2209.13350

Authors: Saripinar, Lutfiye, Kisa, Deniz Hande, Ozdemir, Mehmet Akif, Guren, Onan

Institution: Izmir Kâtip Çelebi University

Language: English

Topic: Muscle activation and electromyography studies

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4297948118

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