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Article

Reliability and Validity of Clinically Accessible Smart Glove Technologies to Measure Joint Range of Motion

1
School of Computing, Engineering and Intelligent System, Ulster University Magee Campus, Northland Rd, BT48 7JL Londonderry, Ireland
2
School of Science, Letterkenny Institute of Technology, Port Rd, Gortlee, F92 FC93 Letterkenny, Ireland
*
Author to whom correspondence should be addressed.
Academic Editor: Marco Iosa
Sensors 2021, 21(5), 1555; https://doi.org/10.3390/s21051555
Received: 31 December 2020 / Revised: 1 February 2021 / Accepted: 16 February 2021 / Published: 24 February 2021
(This article belongs to the Special Issue State-of-the-Art Sensors Technologies in Ireland 2020)
Capturing hand motions for hand function evaluations is essential in the medical field. For many allied health professionals, measuring joint range of motion (ROM) is an important skill. While the universal goniometer (UG) is the most used clinical tool for measuring joint ROM, developments in current sensor technology are providing clinicians with more measurement possibilities than ever. For rehabilitation and manual dexterity evaluations, different data gloves have been developed. However, the reliability and validity of sensor technologies when used within a smart device remain somewhat unclear. This study proposes a novel electronically controlled sensor monitoring system (ECSMS) to obtain the static and dynamic parameters of various sensor technologies for both data gloves and individual sensor evaluation. Similarly, the ECSMS was designed to closely mimic a human finger joint, to have total control over the joint, and to have an exceptionally high precision. In addition, the ECSMS device can closely mimic the movements of the finger from hyperextension to a maximum ROM beyond any person’s finger joint. Due to the modular design, the ECSMS’s sensor monitoring board is independent and extensible to include various technologies for examination. Additionally, by putting these sensory devices through multiple tests, the system accurately measures the characteristics of any rotary/linear sensor in and out of a glove. Moreover, the ECSMS tracks the movement of all types of sensors with respect to the angle values of finger joints. In order to demonstrate the effectiveness of sensory devices, the ECSMS was first validated against a recognised secondary device with an accuracy and resolution of 0.1°. Once validated, the system simultaneously determines real angles alongside the hand monitoring device or sensor. Due to its unique design, the system is independent of the gloves/sensors that were tested and can be used as a gold standard to realise more medical equipment/applications in the future. Consequently, this design greatly enhances testing measures within research contact and even non-contact systems. In conclusion, the ECSMS will benefit in the design of data glove technologies in the future because it provides crucial evidence of sensor characteristics. Similarly, this design greatly enhances the stability and maintainability of sensor assessments by eliminating unwanted errors. These findings provide ample evidence for clinicians to support the use of sensory devices that can calculate joint motion in place of goniometers. View Full-Text
Keywords: rheumatoid arthritis; Parkinson’s disease; stroke recovery; smart sensing; data gloves; joint measurement; rehabilitation; measurement system rheumatoid arthritis; Parkinson’s disease; stroke recovery; smart sensing; data gloves; joint measurement; rehabilitation; measurement system
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MDPI and ACS Style

Henderson, J.; Condell, J.; Connolly, J.; Kelly, D.; Curran, K. Reliability and Validity of Clinically Accessible Smart Glove Technologies to Measure Joint Range of Motion. Sensors 2021, 21, 1555. https://doi.org/10.3390/s21051555

AMA Style

Henderson J, Condell J, Connolly J, Kelly D, Curran K. Reliability and Validity of Clinically Accessible Smart Glove Technologies to Measure Joint Range of Motion. Sensors. 2021; 21(5):1555. https://doi.org/10.3390/s21051555

Chicago/Turabian Style

Henderson, Jeffrey, Joan Condell, James Connolly, Daniel Kelly, and Kevin Curran. 2021. "Reliability and Validity of Clinically Accessible Smart Glove Technologies to Measure Joint Range of Motion" Sensors 21, no. 5: 1555. https://doi.org/10.3390/s21051555

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