scholarly journals An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

10.3791/60857 ◽  
2020 ◽  
Author(s):  
Bram J.C. Bastiaansen ◽  
Erik Wilmes ◽  
Michel S. Brink ◽  
Cornelis J. de Ruiter ◽  
Geert J.P. Savelsbergh ◽  
...  
Keyword(s):  
Knee Joint ◽  
Inertial Measurement Unit ◽  
Team Sport ◽  
Joint Kinematics ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Knee Joint Kinematics

scholarly journals Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor

2019 ◽  
Vol 6 ◽  
pp. 205566831986854 ◽  
Author(s):  
Rob Argent ◽  
Sean Drummond ◽  
Alexandria Remus ◽  
Martin O’Reilly ◽  
Brian Caulfield
Keyword(s):  
Machine Learning ◽  
Knee Joint ◽  
Inertial Measurement Unit ◽  
Joint Angle ◽  
Machine Learning Algorithms ◽  
Measurement Unit ◽  
Reference Standard ◽  
Inertial Measurement ◽  
Knee Joint Angle ◽  
Single Sensor

Introduction Joint angle measurement is an important objective marker in rehabilitation. Inertial measurement units may provide an accurate and reliable method of joint angle assessment. The objective of this study was to assess whether a single sensor with the application of machine learning algorithms could accurately measure hip and knee joint angle, and investigate the effect of inertial measurement unit orientation algorithms and person-specific variables on accuracy. Methods Fourteen healthy participants completed eight rehabilitation exercises with kinematic data captured by a 3D motion capture system, used as the reference standard, and a wearable inertial measurement unit. Joint angle was calculated from the single inertial measurement unit using four machine learning models, and was compared to the reference standard to evaluate accuracy. Results Average root-mean-squared error for the best performing algorithms across all exercises was 4.81° (SD = 1.89). The use of an inertial measurement unit orientation algorithm as a pre-processing step improved accuracy; however, the addition of person-specific variables increased error with average RMSE 4.99° (SD = 1.83°). Conclusions Hip and knee joint angle can be measured with a good degree of accuracy from a single inertial measurement unit using machine learning. This offers the ability to monitor and record dynamic joint angle with a single sensor outside of the clinic.

199 A Validation Study of Knee Joint Movement Measurement by Using Inertial Measurement Unit Sensors

2016 ◽  
Vol 2016.51 (0) ◽  
pp. 195-196
Author(s):  
Akira KOMATSU ◽  
Takehiro IWAMI ◽  
Kimio SAITO ◽  
Kazutoshi HATAKEYAMA ◽  
Manabu AKAGAWA ◽  
...  
Keyword(s):  
Knee Joint ◽  
Validation Study ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Joint Movement ◽  
Inertial Measurement

scholarly journals Development of a Lower Limb Finite Element Musculoskeletal Gait Simulation Framework Driven Solely by Inertial Measurement Unit Sensors

Biomechanics ◽  
2021 ◽  
Vol 1 (3) ◽  
pp. 293-306
Author(s):  
Sentong Wang ◽  
Kazunori Hase ◽  
Susumu Ota
Keyword(s):  
Finite Element ◽  
Knee Joint ◽  
Contact Mechanics ◽  
Lower Limb ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Muscle Forces ◽  
Inertial Measurement ◽  
Medial Side ◽  
Subject Specific

Finite element musculoskeletal (FEMS) approaches using concurrent musculoskeletal and finite element models driven by motion data such as marker-based motion trajectory can provide insight into the interactions between the knee joint secondary kinematics, contact mechanics, and muscle forces in subject-specific biomechanical investigations. However, these data-driven FEMS systems have a major disadvantage that makes them challenging to apply in clinical environments, i.e., they require expensive and inconvenient equipment for data acquisition. In this study, we developed an FEMS model of the lower limb driven solely by inertial measurement unit sensors that include the tissue geometries of the entire knee joint, and that combine modeling of 16 muscles into a single framework. The model requires only the angular velocities and accelerations measured by the sensors as input. The target outputs (knee contact mechanics, secondary kinematics, and muscle forces) are predicted from the convergence results of iterative calculations of muscle force optimization and knee contact mechanics. To evaluate its accuracy, the model was compared with in vivo experimental data during gait. The maximum contact pressure (11.3 MPa) occurred on the medial side of the cartilage at the maximum loading response. The developed framework combines measurement convenience and accurate modeling, and shows promise for clinical applications aimed at understanding subject-specific biomechanics.

scholarly journals Monitoring of Hip and Knee Joint Angles Using a Single Inertial Measurement Unit During Lower Limb Rehabilitation

2016 ◽  
Vol 16 (6) ◽  
pp. 1557-1564 ◽  
Author(s):  
Vincent Bonnet ◽  
Vladimir Joukov ◽  
Dana Kulic ◽  
Philippe Fraisse ◽  
Nacim Ramdani ◽  
...  
Keyword(s):  
Knee Joint ◽  
Lower Limb ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Joint Angles ◽  
Inertial Measurement ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation

Studies on the Measurement of the Knee Joint Motion Using Inertial Measurement Unit

2016 ◽  
Vol 2016.52 (0) ◽  
pp. 409
Author(s):  
Akira KOMATSU ◽  
Atsuya YAGI ◽  
Takehiro IWAMI ◽  
Yoshikazu KOBAYASHI ◽  
Kimio SAITO ◽  
...  
Keyword(s):  
Knee Joint ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Joint Motion ◽  
Inertial Measurement ◽  
Knee Joint Motion

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.

A Computationally Efficient Lower Limb Finite Element Musculoskeletal Framework Directly Driven Solely by Inertial Measurement Unit Sensors

2021 ◽  
Author(s):  
Sentong Wang ◽  
Kazunori Hase ◽  
Susumu Ota
Keyword(s):  
Finite Element ◽  
Knee Joint ◽  
Contact Mechanics ◽  
Lower Limb ◽  
Inertial Measurement Unit ◽  
Contact Analysis ◽  
Measurement Unit ◽  
Muscle Forces ◽  
Computationally Efficient ◽  
Inertial Measurement

Abstract Finite element musculoskeletal (FEMS) approaches using concurrent musculoskeletal and finite element models driven by motion data such as marker-based motion trajectory can provide insight into the interactions between the knee joint secondary kinematics, contact mechanics, and muscle forces in subject-specific biomechanical investigations. However, these data-driven FEMS systems have two major disadvantages that make them challenging to apply in clinical environments: they are computationally expensive and they require expensive and inconvenient equipment for data acquisition. In this study, we developed an FEMS model of the lower limb driven solely by inertial measurement unit sensors that includes the tissue geometries of the entire knee joint and combines muscle modeling and elastic foundation theory-based contact analysis of knee into a single framework. The model requires only the angular velocities and accelerations measured by the sensors as input, and the target outputs (knee contact mechanics, secondary kinematics, and muscle forces) are predicted from the convergence results of iterative calculations of muscle force optimization and knee contact mechanics. To evaluate its accuracy, the model was compared with in vivo experimental data during gait. The maximum contact pressure (12.6 MPa) in the rigid body contact analysis occurred on the medial side of the cartilage at the maximum loading response. The proposed computationally efficient framework drastically reduced the computational time (97.5% reduction) in comparison with the conventional deformable finite element analysis. The developed framework combines measurement convenience and computational efficiency and shows promise for clinical applications.

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