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All-ConvNet: A lightweight all CNN for neuromuscular activity recognition using instantaneous high-density surface EMG images

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Islam, M. R., Massicotte, D. et Zhu, W. P. (2020, May 25-28). All-ConvNet: A lightweight all CNN for neuromuscular activity recognition using instantaneous high-density surface EMG images. Dans 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Dubrovnik, Croatia.

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Résumé

Neuromuscular activity recognition using low-resolution instantaneous high-density surface electromyography (HD-sEMG) images present a great challenge. The recent result shows the high potentiality and hence opens up new avenues for the development of more fluid and natural muscle-computer interfaces. However, the existing approaches employed a very large deep ConvNet, which requires learning >5.63 million training parameters only during fine-tuning and pre-trained on a very large-scale labeled HD-sEMG training datasets, as a result, it makes high-end resource bounded and computationally expensive. To overcome this problem, we propose a lightweight All-ConvNet model that consists solely of convolutional layers, a simple yet efficient framework for learning instantaneous HD-sEMG images from scratch through random initialization. Without using any pre-trained models, our proposed lightweight All-ConvNet demonstrate very competitive or even state of the art performance on a current benchmarks HD-sEMG dataset, while requires learning only ~460k training parameters and using ~12xsmaller dataset. The experimental results proved that the proposed lightweight All-ConvNet is highly effective for learning discriminative features for low-resolution instantaneous HD-sEMG image recognition and low-latency processing especially in the data and high-end resource constrained scenarios.

Type de document: Document issu d'une conférence ou d'un atelier
Mots-clés libres: Neuromuscular activity recognition All convolutional neural networks Feature learning HD-sEMG Gesture recognition Muscle-computer interface Deep neural networks
Date de dépôt: 09 mai 2022 18:17
Dernière modification: 27 mai 2022 12:07
URI: https://depot-e.uqtr.ca/id/eprint/10134

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