Ayolde, E, Zaidi, SAR orcid.org/0000-0003-1969-3727, Scott, J et al. (4 more authors) (2021) A Weft Knit Data Glove. IEEE Transactions on Instrumentation and Measurement, 70. ISSN 0018-9456
Abstract
Rehabilitation of stroke survivors can be expedited by employing an exoskeleton. The exercises are designed such that both hands move in synergy. In this regard, often, motion capture data from the healthy hand is used to derive control behavior for the exoskeleton. Therefore, data gloves can provide a low-cost solution for the motion capture of the joints in the hand. However, current data gloves are bulky, inaccurate, or inconsistent. These disadvantages are inherited because the conventional design of a glove involves an external attachment that degrades overtime and causes inaccuracies. This article presents a weft knit data glove whose sensors and support structure are manufactured in the same fabrication process, thus removing the need for an external attachment. The glove is made by knitting multifilament conductive yarn and an elastomeric yarn using WholeGarment technology. Furthermore, we present a detailed electromechanical model of the sensors alongside its experimental validation. In addition, the reliability of the glove is verified experimentally. Finally, machine learning algorithms are implemented for classifying the posture of hand on the basis of sensor data histograms.
Metadata
Item Type: | Article |
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Authors/Creators: |
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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: | Classification, data glove, electromechanical modeling, wearable, weft knit sensor |
Dates: |
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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: | 30 Apr 2021 09:59 |
Last Modified: | 30 Apr 2021 09:59 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TIM.2021.3068173 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:173548 |