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Improving Kinematic Accuracy of Soft Wearable Data Gloves by Optimizing Sensor Locations.

MedStar author(s):

Citation: Sensors. 16(6), 2016 May 26PMID: 27240364Institution: MedStar National Rehabilitation NetworkForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: *Biosensing Techniques/is [Instrumentation] | *Biosensing Techniques/mt [Methods] | *Wearable Electronic Devices | Hand/ph [Physiology] | Humans | Range of Motion, Articular/ph [Physiology] | Thumb/ph [Physiology]Year: 2016 ISSN:

1424-8220

Name of journal: Sensors (Basel, Switzerland)Abstract: Bending sensors enable compact, wearable designs when used for measuring hand configurations in data gloves. While existing data gloves can accurately measure angular displacement of the finger and distal thumb joints, accurate measurement of thumb carpometacarpal (CMC) joint movements remains challenging due to crosstalk between the multi-sensor outputs required to measure the degrees of freedom (DOF). To properly measure CMC-joint configurations, sensor locations that minimize sensor crosstalk must be identified. This paper presents a novel approach to identifying optimal sensor locations. Three-dimensional hand surface data from ten subjects was collected in multiple thumb postures with varied CMC-joint flexion and abduction angles. For each posture, scanned CMC-joint contours were used to estimate CMC-joint flexion and abduction angles by varying the positions and orientations of two bending sensors. Optimal sensor locations were estimated by the least squares method, which minimized the difference between the true CMC-joint angles and the joint angle estimates. Finally, the resultant optimal sensor locations were experimentally validated. Placing sensors at the optimal locations, CMC-joint angle measurement accuracies improved (flexion, 2.8degree +/- 1.9degree; abduction, 1.9degree +/- 1.2degree). The proposed method for improving the accuracy of the sensing system can be extended to other types of soft wearable measurement devices.All authors: Kim DH, Lee SW, Park HSFiscal year: FY2016Digital Object Identifier:

https://dx.doi.org/10.3390/s16060766

Date added to catalog: 2017-05-24

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Title notes ( 2 )

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode
Journal Article	MedStar Authors Catalog	Article	27240364	Available		27240364

Bending sensors enable compact, wearable designs when used for measuring hand configurations in data gloves. While existing data gloves can accurately measure angular displacement of the finger and distal thumb joints, accurate measurement of thumb carpometacarpal (CMC) joint movements remains challenging due to crosstalk between the multi-sensor outputs required to measure the degrees of freedom (DOF). To properly measure CMC-joint configurations, sensor locations that minimize sensor crosstalk must be identified. This paper presents a novel approach to identifying optimal sensor locations. Three-dimensional hand surface data from ten subjects was collected in multiple thumb postures with varied CMC-joint flexion and abduction angles. For each posture, scanned CMC-joint contours were used to estimate CMC-joint flexion and abduction angles by varying the positions and orientations of two bending sensors. Optimal sensor locations were estimated by the least squares method, which minimized the difference between the true CMC-joint angles and the joint angle estimates. Finally, the resultant optimal sensor locations were experimentally validated. Placing sensors at the optimal locations, CMC-joint angle measurement accuracies improved (flexion, 2.8degree +/- 1.9degree; abduction, 1.9degree +/- 1.2degree). The proposed method for improving the accuracy of the sensing system can be extended to other types of soft wearable measurement devices.

English