A temporal-to-spatial deep convolutional neural network for classification of hand movements from multichannel electromyography data

TL;DR

This paper introduces a novel temporal-to-spatial CNN architecture for classifying hand movements from multichannel sEMG data, demonstrating improved accuracy over existing methods across different datasets.

Contribution

The paper proposes a new TtS CNN design that processes each sEMG channel separately for temporal features before spatially combining them, enhancing gesture classification performance.

Findings

TtS CNN achieved 66.6% accuracy on database 1.

TtS CNN achieved 67.8% accuracy on database 2.

Outperformed existing classifiers with statistical significance.

Abstract

Deep convolutional neural networks (CNNs) are appealing for the purpose of classification of hand movements from surface electromyography (sEMG) data because they have the ability to perform automated person-specific feature extraction from raw data. In this paper, we make the novel contribution of proposing and evaluating a design for the early processing layers in the deep CNN for multichannel sEMG. Specifically, we propose a novel temporal-to-spatial (TtS) CNN architecture, where the first layer performs convolution separately on each sEMG channel to extract temporal features. This is motivated by the idea that sEMG signals in each channel are mediated by one or a small subset of muscles, whose temporal activation patterns are associated with the signature features of a gesture. The temporal layer captures these signature features for each channel separately, which are then spatially mixed in successive layers to recognise a specific gesture. A practical advantage is that this approach also makes the CNN simple to design for different sample rates. We use NinaPro database 1 (27 subjects and 52 movements + rest), sampled at 100 Hz, and database 2 (40 subjects and 40 movements + rest), sampled at 2 kHz, to evaluate our proposed CNN design. We benchmark against a feature-based support vector machine (SVM) classifier, two CNNs from the literature, and an additional standard design of CNN. We find that our novel TtS CNN design achieves 66.6% per-class accuracy on database 1, and 67.8% on database 2, and that the TtS CNN outperforms all other compared classifiers using a statistical hypothesis test at the 2% significance level.