# Transfer Learning for sEMG-based Hand Gesture Classification using Deep Learning in a Master-Slave Architecture

arXiv: https://arxiv.org/abs/2005.03460 · HTML: https://mlsanity.com/paper/2005.03460/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond](https://mlsanity.com/paper/2210.06295/) (2022)

## Abstract (arXiv)

Recent advancements in diagnostic learning and development of gesture-based human machine interfaces have driven surface electromyography (sEMG) towards significant importance. Analysis of hand gestures requires an accurate assessment of sEMG signals. The proposed work presents a novel sequential master-slave architecture consisting of deep neural networks (DNNs) for classification of signs from the Indian sign language using signals recorded from multiple sEMG channels. The performance of the master-slave network is augmented by leveraging additional synthetic feature data generated by long short term memory networks. Performance of the proposed network is compared to that of a conventional DNN prior to and after the addition of synthetic data. Up to 14% improvement is observed in the conventional DNN and up to 9% improvement in master-slave network on addition of synthetic data with an average accuracy value of 93.5% asserting the suitability of the proposed approach.