Accuracy of Wearable Sensor Technology in Hand Goniometry: A Systematic Review. (March 2023)

• Record Type: Journal Article
• Authors: Avila, Francisco R.; Carter, Rickey E.; McLeod, Christopher J.; Bruce, Charles J.; Giardi, Davide; Guliyeva, Gunel; Forte, Antonio J.
• Is Part Of: Hand. Volume 18:Number 2(2023)
• Page Start: 340
• Page End: 348
• Publication Date: 2023-03
• Subjects: hand goniometry -- sensor technology -- sensor glove -- hand range of motion -- goniometer accuracy -- hand rehabilitation -- wearable sensor technology -- bend sensor -- inertial sensor -- manual goniometry; Hand -- Surgery -- Periodicals; Hand -- Surgery; Periodicals; 617.57005
• DOI: 10.1177/15589447211014606
• Languages: English
• ISSNs: 1558-9447

Abstract

Background: Wearable devices and sensor technology provide objective, unbiased range of motion measurements that help health care professionals overcome the hindrances of protractor-based goniometry. This review aims to analyze the accuracy of existing wearable sensor technologies for hand range of motion measurement and identify the most accurate one. Methods: We performed a systematic review by searching PubMed, CINAHL, and Embase for studies evaluating wearable sensor technology in hand range of motion assessment. Keywords used for the inquiry were related to wearable devices and hand goniometry. Results: Of the 71 studies, 11 met the inclusion criteria. Ten studies evaluated gloves and 1 evaluated a wristband. The most common types of sensors used were bend sensors, followed by inertial sensors, Hall effect sensors, and magnetometers. Most studies compared wearable devices with manual goniometry, achieving optimal accuracy. Although most of the devices reached adequate levels of measurement error, accuracy evaluation in the reviewed studies might be subject to bias owing to the use of poorly reliable measurement techniques for comparison of the devices. Conclusion: Gloves using inertial sensors were the most accurate. Future studies should use different comparison techniques, such as infrared camera–based goniometry or virtual motion tracking, to evaluate the performance of wearable devices.