Closing the Wearable Gap—Part VIII: A Validation Study for a Smart Knee Brace to Capture Knee Joint Kinematics

Abstract

Background: Wearable technology is used by clinicians and researchers and play a critical role in biomechanical assessments and rehabilitation. Objective: The purpose of this research is to validate a soft robotic stretch (SRS) sensor embedded in a compression knee brace (smart knee brace) against a motion capture system focusing on knee joint kinematics. Methods: Sixteen participants donned the smart knee brace and completed three separate tasks: non-weight bearing knee flexion/extension, bodyweight air squats, and gait trials. Adjusted R2 for goodness of fit (R2), root mean square error (RMSE), and mean absolute error (MAE) between the SRS sensor and motion capture kinematic data for all three tasks were assessed. Results: For knee flexion/extension: R2 = 0.799, RMSE = 5.470, MAE = 4.560; for bodyweight air squats: R2 = 0.957, RMSE = 8.127, MAE = 6.870; and for gait trials: R2 = 0.565, RMSE = 9.190, MAE = 7.530 were observed. Conclusions: The smart knee brace demonstrated a higher goodness of fit and accuracy during weight-bearing air squats followed by non-weight bearing knee flexion/extension and a lower goodness of fit and accuracy during gait, which can be attributed to the SRS sensor position and orientation, rather than range of motion achieved in each task.

Effects of Hip Bracing on Gait Biomechanics, Pain and Function in Subjects With Mild to Moderate Hip Osteoarthritis

“…Application of markers on clothing increases the risk of relative movement between the bony segment and the marker ( Milner, 2008 ). The application of other methods such as clusters, often reported for knee brace analyses ( Focke et al, 2020 ; Turner et al, 2021 ), is not applicable for the hip joint as the pelvis was entirely covered by the brace and adjoining segments (thighs and torso) possess large portions of wobbling mass. However, as the pelvis belt of the brace was fitted very tightly, relative movement between the pelvis and the brace is probably small but cannot be fully excluded.…”

Closing the Wearable Gap: Foot–ankle kinematic modeling via deep learning models based on a smart sock wearable

“…In conjunction with recent advances in deep neural networks, wearable device development provides the grounds to address the current gaps in gait analysis systems. This is the next study in our Closing the Wearable Gap (CWG) research (Luczak et al, 2018 ; Chander et al, 2019 ; Saucier et al, 2019a , b ; Davarzani et al, 2020 ; Luczak et al, 2020b ; Talegaonkar et al, 2020 ; Carroll et al, 2021 ; Turner et al, 2021 ) with the ultimate goal of designing a wearable solution “from the ground up” (Luczak et al, 2018 ) capable of accurately measuring kinematic and kinetic features of the foot and ankle during gait movement. The proposed study implements a wearable prototype designed by the research team, based on soft robotic sensors (SRS) embedded into a sock to track foot–ankle movement on a treadmill at varying speeds using deep neural networks.…”

“…While several previous research exists on ideal sensor placement for assessing gait (Boerema et al, 2014 ; Engineering and Teichmann, 2016 ; Mokhlespour Esfahani and Nussbaum, 2018 ), these papers predominantly use accelerometer-based sensors, which behave differently from the stretch sensors used in this project. With the linear relationship of change in capacitance or resistance with the stretch of the sensors, these sensors have already been validated against motion capture systems to efficiently capture joint kinematics, when placed across a joint axis (Luczak et al, 2018 ; Chander et al, 2019 ; Saucier et al, 2019a , b ; Davarzani et al, 2020 ; Luczak et al, 2020b ; Talegaonkar et al, 2020 ; Carroll et al, 2021 ; Turner et al, 2021 ). Hence, the positioning of these sensors was determined based on our previous research to accurately capture joint kinematics.…”

Validation of 3D Knee Kinematics during Gait on Treadmill with an Instrumented Knee Brace

“…The monitoring system must be minimally burdensome for the patient, in order to obtain natural movement data and to ensure high patient acceptance levels regarding the technology. Therefore, quick, easy-to-use calibration is increasingly becoming a benefit for remote patient monitoring [ 14 , 15 ].…”