A new framework for classification of multi-category hand grasps using EMG signals

作者：

Highlights：

• Adaptive Boost k-means (AB-k-means) is designed to classify EMG signals.

• Logarithmic Spectrogram Image is employed to extract EMG features.

• AB-k-means classifier categorises hand movements with high classification accuracy.

• A feature selection model is designed to select the most representative features.

摘要

•Adaptive Boost k-means (AB-k-means) is designed to classify EMG signals.•Logarithmic Spectrogram Image is employed to extract EMG features.•AB-k-means classifier categorises hand movements with high classification accuracy.•A feature selection model is designed to select the most representative features.

论文关键词：EMG,LSGS,AB-k-means,Hand grasps,Feature extraction

论文评审过程：Received 10 April 2020, Revised 10 December 2020, Accepted 23 December 2020, Available online 28 December 2020, Version of Record 6 January 2021.

论文官网地址：https://doi.org/10.1016/j.artmed.2020.102005