  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.
