What is it about?

A classification framework for hand gestures using Electromyography (EMG) signals in prosthetic hands is presented. Leveraging the multi-scale characteristics and temporal nature of EMG signals, a Convolutional Neural Network (CNN) is used to extract multi-scale features and classify them with spatial-temporal attention.

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Why is it important?

The proposed model achieves classification accuracies of 93.4%, 92.8%, 91.3%, and 94.1% for Ninapro DB1, DB2, DB5, and DB7 respectively, thereby enhancing the confidence of prosthetic hand users.


This innovative approach capitalizes on the intricate multi-scale characteristics and temporal dynamics inherent in EMG signals. By employing a Convolutional Neural Network (CNN), the framework effectively extracts multi-scale features and employs spatial-temporal attention for classification.

Dr Mahiban Lindsay N
Hindustan University

Read the Original

This page is a summary of: Multi-scale EMG classification with spatial-temporal attention for prosthetic hands, Computer Methods in Biomechanics & Biomedical Engineering, November 2023, Taylor & Francis,
DOI: 10.1080/10255842.2023.2287419.
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