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Lookup NU author(s): Emmanuel AYODELE, Dr Jane Scott
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Grasp classification using data gloves can enable therapists to monitor patients efficiently by providing concise information about the activities performed by these patients. Although, classical machine learning algorithms have been applied in grasp classification, they require manual feature extraction to achieve high accuracy. In contrast, convolutional neural networks (CNNs) have outperformed popular machine learning algorithms in several classification scenarios because of their ability to extract features automatically from raw data. However, they have not been implemented on grasp classification using a data glove. In this study, we apply a CNN in grasp classification using a piezoresistive textile data glove knitted from conductive yarn and an elastomeric yarn. The data glove was used to collect data from five participants who grasped thirty objects each following Schlesinger’s taxonomy. We investigate a CNN’s performance in two scenarios where the validation objects are known and unknown. Our results show that a simple CNN architecture outperformed k-nn, Gaussian SVM, and Decision Tree algorithms in both scenarios in terms of the classification accuracy.
Author(s): Ayodele E, Bao T, Zaidi SAR, Hayajneh A, Scott J, Zhang Z-Q, McLernon D
Publication type: Article
Publication status: Published
Journal: IEEE Sensors Journal
Year: 2021
Volume: 21
Issue: 9
Pages: 10824-10833
Print publication date: 01/05/2021
Online publication date: 12/02/2021
Acceptance date: 05/02/2021
ISSN (print): 1530-437X
ISSN (electronic): 1558-1748
Publisher: IEEE
URL: https://doi.org/10.1109/JSEN.2021.3059028
DOI: 10.1109/JSEN.2021.3059028
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