Classification of Functional Grasps Using Hybrid CNN/LSTM Network

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Gestures made by a human can be classified using Electromyography (EMG) sig-nals collected from the forearm; even with low frequency devices. Numerous steps are required from data collection and pre-processing through to final classi-fication. Traditionally, an important part of EMG signal classification is extract-ing features from the raw signal to reduce dimensionality. It is predominantly car-ried out manually before the signals are input into a neural network. In this re-search we successfully used a CNN to extract the features automatically and an LSTM layer was utilized to classify the gestures. This network architecture re-moves a step in the gesture classification process. Using the raw signals input into a CNN/LSTM hybrid increased classification when compared with an LSTM net-work 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
PublisherSPRINGER LINK
Chapter3
Pages345-363
Number of pages18
Volume95
ISBN (Electronic)978-981-16-6636-0
ISBN (Print)978-981-16-6635-3
DOIs
Publication statusPublished - 4 Dec 2021

Publication series

NameLecture Notes on Data Engineering and Communications Technologies
PublisherSpringer
Volume95
ISSN (Print)2367-4512
ISSN (Electronic)2367-4520

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