Abstract
Wearable technology has been developed in recent years to monitor biomechanical variables in less restricted environments and in a more affordable way than optical motion capture systems. This paper proposes the development of a 3D printed knee wearable goniometer that uses a Hall-effect sensor to measure the knee flexion angle, which works with a mobile app that shows the angle in real-time as well as the activity the user is performing (standing, sitting, or walking). Detection of the activity is done through an algorithm that uses the knee angle and angular speeds as inputs. The measurements of the wearable are compared with a commercial goniometer, and, with the Aktos-t system, a commercial motion capture system based on inertial sensors, at three speeds of gait (4.0 km/h, 4.5 km/h, and 5.0 km/h) in nine participants. Specifically, the four differences between maximum and minimum peaks in the gait cycle, starting with heel-strike, were compared by using the mean absolute error, which was between 2.46 and 12.49 on average. In addition, the algorithm was able to predict the three activities during online testing in one participant and detected on average 94.66% of the gait cycles performed by the participants during offline testing.
Highlights
This paper proposes the development of a 3D printed knee wearable goniometer that uses a Halleffect sensor to measure the knee flexion angle, which works with a mobile app that shows the angle in real-time as well as the activity the user is performing
To evaluate how well the algorithm detected the activity of walking, was obtained by dividing the number of detected gait cycles by the total passed through the algorithm
Mean Absolute Error (MAE) shows that the error of the wearable varies for different subjects
Summary
This paper proposes the development of a 3D printed knee wearable goniometer that uses a Halleffect sensor to measure the knee flexion angle, which works with a mobile app that shows the angle in real-time as well as the activity the user is performing (standing, sitting, or walking). For measuring the PROM, medical professionals commonly use goniometers [1], while, during AROM, motion capture systems able to measure the functional ROM variables in real-time must be necessary. Optical motion systems involve cameras and markers; they are the gold standard for measuring kinematic variables but involve costs and time to get ready Due to this limitation, the use of wearable technology as motion capture systems has been promoted [2,3,4,5,6], as they have the potential to measure kinematic parameters during daily activities and in less restricted environments due to their portability, allowing continuous monitoring of the joint [4,5]. In the case of academic institutions, diverse systems using potentiometers [10], IMUs [11,12], Hall-effect sensors [13], and knitted piezoresistive fabric technology [14]
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