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IMU sensor-based data glove for finger joint measurement

The methods used to quantify finger range of motion significantly influence how hand disability is reported. To date, the accuracy of sensors being utilized in data gloves from the literature has been ascertained yet need further analysis. This paper presents a sensor-based data glove for finger joi...

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Main Authors: Hazman, Muhammad Ajwad Wa’ie, Mohd. Nordin, Ili Najaa Aimi, Mohd. Noh, Faridah Hanim, Khamis, Nurulaqilla, Razif, M. R. M., Faudzi, Ahmad Athif, Mohd. Hanif, Asyikin Sasha
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2020
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/90989/1/Ahmad%27AthifMohdFaudzi2020_IMUSensorBasedDataGloveforFingerJointMeasurement.pdf
http://eprints.utm.my/id/eprint/90989/
http://dx.doi.org/10.11591/ijeecs.v20.i1.pp82-88
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spelling my.utm.909892021-05-31T13:29:02Z http://eprints.utm.my/id/eprint/90989/ IMU sensor-based data glove for finger joint measurement Hazman, Muhammad Ajwad Wa’ie Mohd. Nordin, Ili Najaa Aimi Mohd. Noh, Faridah Hanim Khamis, Nurulaqilla Razif, M. R. M. Faudzi, Ahmad Athif Mohd. Hanif, Asyikin Sasha T Technology (General) The methods used to quantify finger range of motion significantly influence how hand disability is reported. To date, the accuracy of sensors being utilized in data gloves from the literature has been ascertained yet need further analysis. This paper presents a sensor-based data glove for finger joint measurement developed for collecting a range of motion data of distal interphalangeal, proximal interphalangeal and metacarpophalangeal finger joints of an index finger. In this study, three inertial measurement sensors and two flexible bend sensors were attached to the finger joint points on the glove to detect angle displacement. The angle displacements were acquired using Arduino and MATLAB software interface. Goniometry was used to allow accurate comparative measurement. Low percentage of error resulted from inertial measurement unit ( 0.81 % to 5.41 % ), indicates high accuracy. On the other hand, flexible bend sensor shows low accuracy (11.11 % to 19.35 % error). In conclusion, the inertial measurement unit sensor, MPU-6050 can be a reliable solution for tracking the progress of finger rehabilitation exercises. In order to motivate patients to adhere to the therapy exercises, interactive rehabilitation game will be developed in the future incorporating MPU-6050 sensors on all five fingers. Institute of Advanced Engineering and Science 2020-10 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/90989/1/Ahmad%27AthifMohdFaudzi2020_IMUSensorBasedDataGloveforFingerJointMeasurement.pdf Hazman, Muhammad Ajwad Wa’ie and Mohd. Nordin, Ili Najaa Aimi and Mohd. Noh, Faridah Hanim and Khamis, Nurulaqilla and Razif, M. R. M. and Faudzi, Ahmad Athif and Mohd. Hanif, Asyikin Sasha (2020) IMU sensor-based data glove for finger joint measurement. Indonesian Journal of Electrical Engineering and Computer Science, 20 (1). pp. 82-88. ISSN 2502-4752 http://dx.doi.org/10.11591/ijeecs.v20.i1.pp82-88
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Hazman, Muhammad Ajwad Wa’ie
Mohd. Nordin, Ili Najaa Aimi
Mohd. Noh, Faridah Hanim
Khamis, Nurulaqilla
Razif, M. R. M.
Faudzi, Ahmad Athif
Mohd. Hanif, Asyikin Sasha
IMU sensor-based data glove for finger joint measurement
description The methods used to quantify finger range of motion significantly influence how hand disability is reported. To date, the accuracy of sensors being utilized in data gloves from the literature has been ascertained yet need further analysis. This paper presents a sensor-based data glove for finger joint measurement developed for collecting a range of motion data of distal interphalangeal, proximal interphalangeal and metacarpophalangeal finger joints of an index finger. In this study, three inertial measurement sensors and two flexible bend sensors were attached to the finger joint points on the glove to detect angle displacement. The angle displacements were acquired using Arduino and MATLAB software interface. Goniometry was used to allow accurate comparative measurement. Low percentage of error resulted from inertial measurement unit ( 0.81 % to 5.41 % ), indicates high accuracy. On the other hand, flexible bend sensor shows low accuracy (11.11 % to 19.35 % error). In conclusion, the inertial measurement unit sensor, MPU-6050 can be a reliable solution for tracking the progress of finger rehabilitation exercises. In order to motivate patients to adhere to the therapy exercises, interactive rehabilitation game will be developed in the future incorporating MPU-6050 sensors on all five fingers.
format Article
author Hazman, Muhammad Ajwad Wa’ie
Mohd. Nordin, Ili Najaa Aimi
Mohd. Noh, Faridah Hanim
Khamis, Nurulaqilla
Razif, M. R. M.
Faudzi, Ahmad Athif
Mohd. Hanif, Asyikin Sasha
author_facet Hazman, Muhammad Ajwad Wa’ie
Mohd. Nordin, Ili Najaa Aimi
Mohd. Noh, Faridah Hanim
Khamis, Nurulaqilla
Razif, M. R. M.
Faudzi, Ahmad Athif
Mohd. Hanif, Asyikin Sasha
author_sort Hazman, Muhammad Ajwad Wa’ie
title IMU sensor-based data glove for finger joint measurement
title_short IMU sensor-based data glove for finger joint measurement
title_full IMU sensor-based data glove for finger joint measurement
title_fullStr IMU sensor-based data glove for finger joint measurement
title_full_unstemmed IMU sensor-based data glove for finger joint measurement
title_sort imu sensor-based data glove for finger joint measurement
publisher Institute of Advanced Engineering and Science
publishDate 2020
url http://eprints.utm.my/id/eprint/90989/1/Ahmad%27AthifMohdFaudzi2020_IMUSensorBasedDataGloveforFingerJointMeasurement.pdf
http://eprints.utm.my/id/eprint/90989/
http://dx.doi.org/10.11591/ijeecs.v20.i1.pp82-88
_version_ 1702169629882843136
score 13.211869

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