scholarly journals Muscle co-activation, knee joint kinematics, and ground reaction forces are altered in the operated leg of meniscectomized patients at high risk of knee osteoarthritis

2012 ◽  
Vol 20 ◽  
pp. S96
Author(s):  
J.B. Thorlund ◽  
J. Damgaard ◽  
E.M. Roos ◽  
P. Aagaard
Keyword(s):  
High Risk ◽  
Knee Joint ◽  
Knee Osteoarthritis ◽  
Joint Kinematics ◽  
Ground Reaction Forces ◽  
Co Activation ◽  
Knee Joint Kinematics ◽  
Reaction Forces

scholarly journals Medial and lateral hamstrings and quadriceps co-activation affects knee joint kinematics and ACL elongation: a pilot study

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Benjamin G. Serpell ◽  
Jennie M. Scarvell ◽  
Mark R. Pickering ◽  
Nick B. Ball ◽  
Phillip Newman ◽  
...  
Keyword(s):  
Pilot Study ◽  
Knee Joint ◽  
Joint Kinematics ◽  
Co Activation ◽  
Knee Joint Kinematics

scholarly journals Predicting Knee Joint Kinematics from Wearable Sensor Data in People with Knee Osteoarthritis and Clinical Considerations for Future Machine Learning Models

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 446
Author(s):  
Jay-Shian Tan ◽  
Sawitchaya Tippaya ◽  
Tara Binnie ◽  
Paul Davey ◽  
Kathryn Napier ◽  
...  
Keyword(s):  
Deep Learning ◽  
Knee Joint ◽  
Knee Osteoarthritis ◽  
Sagittal Plane ◽  
Joint Kinematics ◽  
Training Data ◽  
Sensor Data ◽  
Measurement Unit ◽  
Learning Models ◽  
Knee Joint Kinematics

Deep learning models developed to predict knee joint kinematics are usually trained on inertial measurement unit (IMU) data from healthy people and only for the activity of walking. Yet, people with knee osteoarthritis have difficulties with other activities and there are a lack of studies using IMU training data from this population. Our objective was to conduct a proof-of-concept study to determine the feasibility of using IMU training data from people with knee osteoarthritis performing multiple clinically important activities to predict knee joint sagittal plane kinematics using a deep learning approach. We trained a bidirectional long short-term memory model on IMU data from 17 participants with knee osteoarthritis to estimate knee joint flexion kinematics for phases of walking, transitioning to and from a chair, and negotiating stairs. We tested two models, a double-leg model (four IMUs) and a single-leg model (two IMUs). The single-leg model demonstrated less prediction error compared to the double-leg model. Across the different activity phases, RMSE (SD) ranged from 7.04° (2.6) to 11.78° (6.04), MAE (SD) from 5.99° (2.34) to 10.37° (5.44), and Pearson’s R from 0.85 to 0.99 using leave-one-subject-out cross-validation. This study demonstrates the feasibility of using IMU training data from people who have knee osteoarthritis for the prediction of kinematics for multiple clinically relevant activities.

scholarly journals Effect of Knee Osteoarthritis Progression on Knee-Joint Kinematics and Kinetics during Gait

Biomechanisms ◽  
2016 ◽  
Vol 23 (0) ◽  
pp. 129-138
Author(s):  
Ryousuke HATA ◽  
Katsutoshi NISHINO ◽  
Go OMORI ◽  
Yasuharu NAGANO ◽  
Yuji TANABE
Keyword(s):  
Knee Joint ◽  
Knee Osteoarthritis ◽  
Joint Kinematics ◽  
Knee Joint Kinematics ◽  
Osteoarthritis Progression ◽  
Kinematics And Kinetics
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