Classification of Functional Grasps Using Hybrid CNN/LSTM Network

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Gestures made by a human can be classified using Electromyography (EMG) signals collected from the forearm; even with low-frequency devices. Numerous steps are required from data collection and pre-processing through to final classification. Traditionally, an important part of EMG signal classification is extracting features from the raw signal to reduce dimensionality. It is predominantly carried out manually before the signals are input into a neural network. In this research, we successfully used a CNN to extract the features automatically, and an LSTM layer was utilised to classify the gestures. This network architecture removes a step in the gesture classification process. Using the raw signals input into a CNN/LSTM hybrid increased classification when compared with an LSTM network that required features to be manually extracted from the raw signals.

Original languageEnglish
Title of host publicationProceedings of the International Conference on BIg Data, IoT and Machine Learning
EditorsMohammad Shamsul Arefin, M Shamim Kaiser, Anirban Bandyopadhyay, Md Atiqur Rahman Ahad, Kanad Ray
Number of pages19
ISBN (Electronic)978-981-16-6636-0
ISBN (Print)978-981-16-6635-3
Publication statusPublished (in print/issue) - 4 Dec 2021

Publication series

NameLecture Notes on Data Engineering and Communications Technologies
ISSN (Print)2367-4512
ISSN (Electronic)2367-4520

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.


  • CNN
  • Gesture classification
  • LSTM
  • sEMG


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