The Effect of Correction Algorithms on Knee Kinematics and Kinetics during Gait of Patients with Knee Osteoarthritis

In gait analysis, the accuracy of knee joint angles and moments is critical for clinical decision-making. The purpose of this study was to determine the efficacy of two existing algorithms for knee joint axis correction under pathological conditions. Gait data from 20 healthy participants and 20 patients with knee osteoarthritis (OA) were collected using a motion capture system. An algorithm based on Principal Component Analysis (PCA) and a functional joint-based algorithm (FJA) were used to define the knee joint flexion axis. The results show that PCA decreased crosstalk for both groups, and FJA reduced crosstalk in patients with knee OA only. PCA decreased the range of motions of patients with knee OA in the direction of abduction/adduction significantly. There was a significant increase in the maximum knee flexion moment of patients with knee OA by FJA. The results indicate that both algorithms can efficiently reduce crosstalk for gait from patients with knee OA, which can further influence the results of knee joint angles and moments. We recommend that the correction algorithms be applied in clinical gait analysis with patients with knee OA.

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How Do Physical Therapists Approach Management of People With Early Knee Osteoarthritis? A Qualitative Study

Physical Therapy ◽

10.1093/ptj/pzz164 ◽

2019 ◽

Vol 100 (2) ◽

pp. 295-306 ◽

Cited By ~ 1

Author(s):

Crystal MacKay ◽

Gillian A Hawker ◽

Susan B Jaglal

Keyword(s):

Knee Osteoarthritis ◽

Weight Management ◽

Clinical Decision Making ◽

Physical Therapists ◽

Physical Therapist ◽

Continuing Professional Development ◽

Clinical Decision ◽

Therapeutic Interventions ◽

Evidence Based ◽

Knee Oa

Abstract Background Knee osteoarthritis (OA) is a leading cause of disability. There is increasing emphasis on initiating treatment earlier in the disease. Physical therapists are central to the management of OA through the delivery of exercise programs. There is a paucity of research on physical therapists’ perceptions and clinical behaviors related to early knee OA management. Objective The study aimed to explore how physical therapists approached management of early knee OA, with a focus on evidence-based strategies. This is an important first step to begin to optimize care by physical therapists for this population. Design We used a qualitative, descriptive research design. Methods Semistructured interviews were conducted with 33 physical therapists working with people with knee symptoms and/or diagnosed knee OA in community or outpatient settings in Canada. Data were analyzed using thematic analysis. Results Five main themes were constructed: (1) Physical therapists’ experience and training: clinical experiences and continuing professional development informed clinical decision-making. (2) Tailoring treatment from the physical therapist “toolbox:” participants described their toolbox of therapeutic interventions, highlighting the importance of tailoring treatments to people. (3) The central role of exercise and physical activity in management: exercise was consistently recommended by participants. (4) Variability in support for weight management: there was variation related to how participants addressed weight management. (5) Facilitating “buy-in” to management: physical therapists used a range of strategies to gain “buy-in.” Limitations Participants were recruited through a professional association specializing in orthopedic physical therapy and worked an average of 21 years. Conclusions Participants’ accounts emphasized tailoring of interventions, particularly exercises, which is an evidence-based strategy for OA. Findings illuminated variations in management that warrant further exploration to optimize early intervention (eg, weight management, behavior change techniques).

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AB0774 A New Correction Method for Knee Kinematics in 3D GAIT Analysis in Healthy and Knee Osteoarthritis Patients Using Principal Component Analysis (PCA)

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2014-eular.1932 ◽

2014 ◽

Vol 73 (Suppl 2) ◽

pp. 1060.3-1060

Author(s):

A. Baudet ◽

C. Morisset ◽

J. Maillefert ◽

V. Gremeaux ◽

P. Ornetti ◽

...

Keyword(s):

Principal Component Analysis ◽

Knee Osteoarthritis ◽

Gait Analysis ◽

Knee Kinematics ◽

Principal Component ◽

Component Analysis ◽

Correction Method

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Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders

Sensors ◽

10.3390/s20236992 ◽

2020 ◽

Vol 20 (23) ◽

pp. 6992

Author(s):

Rana Zia Ur Rehman ◽

Yuhan Zhou ◽

Silvia Del Din ◽

Lisa Alcock ◽

Clint Hansen ◽

...

Keyword(s):

Gait Analysis ◽

Fall Risk ◽

Clinical Decision Making ◽

Principal Component ◽

Clinical Decision ◽

Poor Quality ◽

Neurological Patients ◽

Stride Duration

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43–99% sensitivity and 48–98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.

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Clinical decision making in spastic bilateral CP – Planning interventions using clinical gait analysis: A case study

Gait & Posture ◽

10.1016/j.gaitpost.2015.06.073 ◽

2015 ◽

Vol 42 ◽

pp. S37

Author(s):

M. Alvela ◽

M. Bergmann ◽

M.-L. Ööpik ◽

Ü. Kruus ◽

K. Englas ◽

...

Keyword(s):

Decision Making ◽

Gait Analysis ◽

Clinical Decision Making ◽

Clinical Decision ◽

Clinical Gait Analysis

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Functional Evaluation of a Personalized Orthosis for Knee Osteoarthritis - A Motion Capture Analysis

Journal of Medical Devices ◽

10.1115/1.4051626 ◽

2021 ◽

Author(s):

Martin Huber ◽

Matthew Eschbach ◽

Kazem Kazerounian ◽

Horea T. Ilies

Keyword(s):

Knee Joint ◽

Knee Osteoarthritis ◽

Motion Capture ◽

Inverse Dynamics ◽

Statistical Significance ◽

Knee Kinematics ◽

Contact Forces ◽

Patient Specific ◽

Functional Evaluation ◽

Shock Absorption

Abstract Knee osteoarthritis (OA) is a disease that compromises the cartilage inside the knee joint, resulting in pain and impaired mobility. Bracing is a common treatment, however currently prescribed braces cannot treat bicompartmental knee OA, fail to consider the muscle weakness that typically accompanies the disease, and utilize hinges that restrict the knee's natural biomechanics. We have developed and evaluated a brace which addresses these shortcomings. This process has respected three principal design goals: reducing the load experienced across the entire knee joint, generating a supportive moment to aid the muscles in shock absorption, and interfering minimally with gait kinematics. Load reduction is achieved via the compression of medial and lateral leaf springs, and magnetorheological dampers provide the supportive moment during knee loading. A novel, personalized joint mechanism replaces a traditional hinge to reduce interference with knee kinematics. Using motion capture gait analysis, we evaluated the basic functionality of a prototype device. We calculated, via inverse dynamics analysis, the reaction forces at the knee joint and the moments generated by the leg muscles during gait. Comparing these values between braced and unbraced trials allowed us to evaluate the system's effectiveness. Kinematic measurements showed the extent to which the brace interfered with natural gait characteristics. Of the three design goals: a reduction in knee contact forces was demonstrated; increased shock absorption was observed, but not to statistical significance; and natural gait was largely preserved. The techniques presented in this paper could lead to improved OA treatment through patient-specific braces.

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Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features

Journal of Healthcare Engineering ◽

10.1155/2021/4123471 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Wenzhi Zhang ◽

Runchuan Li ◽

Shengya Shen ◽

Jinliang Yao ◽

Yan Peng ◽

...

Keyword(s):

Myocardial Infarction ◽

Clinical Decision Making ◽

Human Life ◽

Principal Component ◽

Fusion Rule ◽

Clinical Decision ◽

Gradient Boosting ◽

Discrete Wavelet ◽

Extreme Gradient Boosting ◽

Ventricular Activity

Myocardial infarction (MI) is one of the most common cardiovascular diseases threatening human life. In order to accurately distinguish myocardial infarction and have a good interpretability, the classification method that combines rule features and ventricular activity features is proposed in this paper. Specifically, according to the clinical diagnosis rule and the pathological changes of myocardial infarction on the electrocardiogram, the local information extracted from the Q wave, ST segment, and T wave is computed as the rule feature. All samples of the QT segment are extracted as ventricular activity features. Then, in order to reduce the computational complexity of the ventricular activity features, the effects of Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), and Locality Preserving Projections (LPP) on the extracted ventricular activity features are compared. Combining rule features and ventricular activity features, all the 12 leads features are fused as the ultimate feature vector. Finally, eXtreme Gradient Boosting (XGBoost) is used to identify myocardial infarction, and the overall accuracy rate of 99.86% is obtained on the Physikalisch-Technische Bundesanstalt (PTB) database. This method has a good medical diagnosis basis while improving the accuracy, which is very important for clinical decision-making.

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Prediction of Knee Joint Contact Forces From External Measures Using Principal Component Prediction and Reconstruction

Journal of Applied Biomechanics ◽

10.1123/jab.2017-0262 ◽

2018 ◽

Vol 34 (5) ◽

pp. 419-423 ◽

Cited By ~ 1

Author(s):

Christopher M. Saliba ◽

Allison L. Clouthier ◽

Scott C.E. Brandon ◽

Michael J. Rainbow ◽

Kevin J. Deluzio

Keyword(s):

Knee Joint ◽

Knee Osteoarthritis ◽

Real Time ◽

Contact Force ◽

Haptic Feedback ◽

Principal Component ◽

Contact Forces ◽

Statistical Regression ◽

Joint Contact Force ◽

Joint Contact

Abnormal loading of the knee joint contributes to the pathogenesis of knee osteoarthritis. Gait retraining is a noninvasive intervention that aims to reduce knee loads by providing audible, visual, or haptic feedback of gait parameters. The computational expense of joint contact force prediction has limited real-time feedback to surrogate measures of the contact force, such as the knee adduction moment. We developed a method to predict knee joint contact forces using motion analysis and a statistical regression model that can be implemented in near real-time. Gait waveform variables were deconstructed using principal component analysis, and a linear regression was used to predict the principal component scores of the contact force waveforms. Knee joint contact force waveforms were reconstructed using the predicted scores. We tested our method using a heterogenous population of asymptomatic controls and subjects with knee osteoarthritis. The reconstructed contact force waveforms had mean (SD) root mean square differences of 0.17 (0.05) bodyweight compared with the contact forces predicted by a musculoskeletal model. Our method successfully predicted subject-specific shape features of contact force waveforms and is a potentially powerful tool in biofeedback and clinical gait analysis.

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The Relationship Between Early-Stage Knee Osteoarthritis and Lower-Extremity Alignment, Joint Laxity, and Subjective Scores of Pain, Stiffness, and Function

Journal of Sport Rehabilitation ◽

10.1123/jsr.2014-0170 ◽

2016 ◽

Vol 25 (3) ◽

pp. 213-218

Author(s):

Charlie A. Hicks-Little ◽

Richard D. Peindl ◽

Tricia J. Hubbard-Turner ◽

Mitchell L. Cordova

Keyword(s):

Knee Joint ◽

Lower Extremity ◽

Knee Osteoarthritis ◽

Outcome Measures ◽

Early Stage ◽

Joint Laxity ◽

Knee Oa ◽

Lower Extremity Alignment ◽

Knee Joint Laxity ◽

And Function

Context:Knee osteoarthritis (OA) is a debilitating disease that affects an estimated 27 million Americans. Changes in lowerextremity alignment and joint laxity have been found to redistribute the medial and/or lateral loads at the joint. However, the effect that changes in anteroposterior knee-joint laxity have on lower-extremity alignment and function in individuals with knee OA remains unclear.Objective:To examine anteroposterior knee-joint laxity, lower-extremity alignment, and subjective pain, stiffness, and function scores in individuals with early-stage knee OA and matched controls and to determine if a relationship exists among these measures.Design:Case control.Setting:Sports-medicine research laboratory.Participants:18 participants with knee OA and 18 healthy matched controls.Intervention:Participants completed the Western Ontario McMaster (WOMAC) osteoarthritis questionnaire and were tested for total anteroposterior knee-joint laxity (A-P) and knee-joint alignment (ALIGN).Main Outcome Measures:WOMAC scores, A-P (mm), and ALIGN (°).Results:A significant multivariate main effect for group (Wilks’ Λ = 0.30, F7,26 = 8.58, P < .0001) was found. Knee-OA participants differed in WOMAC scores (P < .0001) but did not differ from healthy controls on ALIGN (P = .49) or total A-P (P = .66). No significant relationships were identified among main outcome measures.Conclusion:These data demonstrate that participants with early-stage knee OA had worse pain, stiffness, and functional outcome scores than the matched controls; however, ALIGN and A-P were no different. There was no association identified among participants’ subjective scores, ALIGN, or A-P measures in this study.

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A Systematic Review of the Associations Between Inverse Dynamics and Musculoskeletal Modeling to Investigate Joint Loading in a Clinical Environment

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2020.603907 ◽

2020 ◽

Vol 8 ◽

Author(s):

Jana Holder ◽

Ursula Trinler ◽

Andrea Meurer ◽

Felix Stief

Keyword(s):

Knee Joint ◽

Clinical Decision Making ◽

Inverse Dynamics ◽

Clinical Decision ◽

Contact Forces ◽

Musculoskeletal Modeling ◽

Joint Loading ◽

Joint Moments ◽

Internal Joint ◽

Joint Contact

The assessment of knee or hip joint loading by external joint moments is mainly used to draw conclusions on clinical decision making. However, the correlation between internal and external loads has not been systematically analyzed. This systematic review aims, therefore, to clarify the relationship between external and internal joint loading measures during gait. A systematic database search was performed to identify appropriate studies for inclusion. In total, 4,554 articles were identified, while 17 articles were finally included in data extraction. External joint loading parameters were calculated using the inverse dynamics approach and internal joint loading parameters by musculoskeletal modeling or instrumented prosthesis. It was found that the medial and total knee joint contact forces as well as hip joint contact forces in the first half of stance can be well predicted using external joint moments in the frontal plane, which is further improved by including the sagittal joint moment. Worse correlations were found for the peak in the second half of stance as well as for internal lateral knee joint contact forces. The estimation of external joint moments is useful for a general statement about the peak in the first half of stance or for the maximal loading. Nevertheless, when investigating diseases as valgus malalignment, the estimation of lateral knee joint contact forces is necessary for clinical decision making because external joint moments could not predict the lateral knee joint loading sufficient enough. Dependent on the clinical question, either estimating the external joint moments by inverse dynamics or internal joint contact forces by musculoskeletal modeling should be used.

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