Ayodele, E, Bao, T orcid.org/0000-0002-1103-2660, Zaidi, SAR orcid.org/0000-0003-1969-3727 et al. (4 more authors) (2021) Grasp Classification with Weft Knit Data Glove using a Convolutional Neural Network. IEEE Sensors Journal, 21 (9). pp. 10824-10833. ISSN 1530-437X
Abstract
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.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Editors: |
|
Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Sensors, Yarn, Data gloves, Contact resistance, Wearable computers, Electromechanical sensors, Resistance |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 11 Feb 2021 15:30 |
Last Modified: | 22 Feb 2022 14:44 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/JSEN.2021.3059028 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:170854 |