Communication
Accuracy of a Low-Cost 3D-Printed Wearable Goniometer for
Measuring Wrist Motion
Calvin Young * , Sarah DeDecker, Drew Anderson , Michele L. Oliver and Karen D. Gordon
Citation: Young, C.; DeDecker, S.;
Anderson, D.; Oliver, M.L.; Gordon,
K.D. Accuracy of a Low-Cost
3D-Printed Wearable Goniometer for
Measuring Wrist Motion. Sensors
2021, 21, 4799. https://doi.org/
10.3390/s21144799
Academic Editors: Pietro Picerno,
Andrea Mannini and Clive D Souza
Received: 4 June 2021
Accepted: 9 July 2021
Published: 14 July 2021
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Attribution (CC BY) license (https://
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4.0/).
School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada; sdedecke@uoguelph.ca (S.D.);
dander04@uoguelph.ca (D.A.); moliver@uoguelph.ca (M.L.O.); kgordon@uoguelph.ca (K.D.G.)
* Correspondence: cyoung02@uoguelph.ca
Abstract:
Wrist motion provides an important metric for disease monitoring and occupational risk
assessment. The collection of wrist kinematics in occupational or other real-world environments could
augment traditional observational or video-analysis based assessment. We have developed a low-cost
3D printed wearable device, capable of being produced on consumer grade desktop 3D printers.
Here we present a preliminary validation of the device against a gold standard optical motion capture
system. Data were collected from 10 participants performing a static angle matching task while seated
at a desk. The wearable device output was significantly correlated with the optical motion capture
system yielding a coefficient of determination (R
2
) of 0.991 and 0.972 for flexion/extension (FE) and
radial/ulnar deviation (RUD) respectively (p < 0.0001). Error was similarly low with a root mean
squared error of 4.9
°
(FE) and 3.9
°
(RUD). Agreement between the two systems was quantified using
Bland–Altman analysis, with bias and 95% limits of agreement of 3.1
° ±
7.4
°
and
−
0.16
° ±
7.7
°
for FE
and RUD, respectively. These results compare favourably with current methods for occupational
assessment, suggesting strong potential for field implementation.
Keywords: wearable device; electromechanical goniometry; occupational biomechanics
1. Introduction
Wrist motion is a valuable metric in many fields, including orthopaedic surgery, hand
and upper extremity rehabilitation and therapy, ergonomics, athletics, and other areas that
relate to the performance of a task involving the hands and upper extremity. The wrist is
frequently described as a two degree of freedom joint, with the largest range of motion
occurring about the flexion/extension (FE) and radial/ulnar deviation (RUD) axes [1].
Wrist motion can be quantified in a variety of ways, including optical motion cap-
ture (OMC), electrogoniometry, and video analysis. OMC is widely considered to be a
benchmark for kinematic measurement methods as it is accurate, non-invasive, widely
used in scientific experimental studies, and does not expose participants to the radiation
associated with imaging-based methods [
1
]. However, there are notable disadvantages
to OMC. The systems are expensive, restricted to a confined laboratory setting or capture
volume, and require significant time and technical expertise for the setup, operation, and
data post processing [
1
,
2
]. Video capture is more widely used in ergonomic assessment,
as it is more portable, and easier to setup and operate, making it practical to use in the
workplace. While some experimental strategies have been successful with the extraction of
continuous kinematics from video data, these strategies have not yet been widely explored
for ergonomic assessment [
3
]. Commonly-used ergonomic assessment methodologies
include rapid upper limb assessment (RULA) [
4
], rapid entire body assessment (REBA) [
5
],
and strain index (SI) [
6
], all of which rely on expert analysis to bin postures into general
ranges for subsequent interpretation.
Electrogoniometry offers a practical alternative to video analysis or OMC-based meth-
ods and has been demonstrated to have good agreement with OMC-based methods, and
higher reliability than video-analysis [1,3]. Wearable ergonomic assessment tools, such as
Sensors 2021, 21, 4799. https://doi.org/10.3390/s21144799 https://www.mdpi.com/journal/sensors
