Analysis of dual-channel surface electromyogram using second-order and higher-order spectral features

Gupta, Rinki; Kulshreshtha, Ankita

doi:10.1109/ccintels.2016.7878199

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…This is done by extracting features from the gestures of each subject. The most prominently used time-domain features are considered here [15][16]. These include: (1)…”

Section: B Synthetic Feature Generationmentioning

confidence: 99%

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

Suri

Gupta

2018

2018 3rd International Conference on Contemporary Computing and Informatics (IC3I)

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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.

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“…This is done by extracting features from the gestures of each subject. The most prominently used time-domain features are considered here [15][16]. These include: (1)…”

Section: B Synthetic Feature Generationmentioning

confidence: 99%