Identifying key gait features associated with the radiological grade of knee osteoarthritis

Gait classification of knee osteoarthritis patients using shoe-embedded internal measurement units sensor

BACKGROUND

Knee osteoarthritis negatively affects the gait of patients, especially that of elderly people. However, the assessment of wearable sensors in knee osteoarthritis patients has been under-researched. During clinical assessments, patients may change their gait patterns under the placebo effect, whereas wearable sensors can be used in any environment.

METHODS

Sixty patients with knee osteoarthritis and 20 control subjects were included in the study. Wearing shoes with an IMU sensor embedded in the insoles, the participants were required to walk along a walkway. The sensor data were collected during the gait. To discriminate between healthy and knee osteoarthritis patients and to classify different subgroups of knee osteoarthritis patients (patients scheduled for surgery vs. patients not scheduled for surgery; bilateral knee osteoarthritis diagnosis vs. unilateral knee osteoarthritis diagnosis), we used a machine learning approach called the support vector machine. A total of 88 features were extracted and used for classification.

FINDINGS

The patients vs. healthy participants were classified with 71% accuracy, 85% sensitivity, and 56% specificity. The "patients scheduled for surgery" vs. "patients not scheduled for surgery" were classified with 83% accuracy, 83% sensitivity, and 81% specificity. The bilateral knee osteoarthritis diagnosis vs. unilateral knee osteoarthritis diagnosis was classified with 81% accuracy, 75% sensitivity, and 79% specificity.

INTERPRETATION

Gait analysis using wearable sensors and machine learning can discriminate between healthy and knee osteoarthritis patients and classify different subgroups with reasonable accuracy, sensitivity, and specificity. The proposed approach requires no complex gait factors and is not limited to controlled laboratory settings.

Estimation of patient-reported outcome measures based on features of knee joint muscle co-activation in advanced knee osteoarthritis

Electromyography (EMG) is considered a potential predictive tool for the severity of knee osteoarthritis (OA) symptoms and functional outcomes. Patient-reported outcome measures (PROMs), such as the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and visual analog scale (VAS), are used to determine the severity of knee OA. We aim to investigate muscle activation and co-contraction patterns through EMG from the lower extremity muscles of patients with advanced knee OA patients and evaluate the effectiveness of an interpretable machine-learning model to estimate the severity of knee OA according to the WOMAC (pain, stiffness, and physical function) and VAS using EMG gait features. To explore neuromuscular gait patterns with knee OA severity, EMG from rectus femoris, medial hamstring, tibialis anterior, and gastrocnemius muscles were recorded from 84 patients diagnosed with advanced knee OA during ground walking. Muscle activation patterns and co-activation indices were calculated over the gait cycle for pairs of medial and lateral muscles. We utilized machine-learning regression models to estimate the severity of knee OA symptoms according to the PROMs using muscle activity and co-contraction features. Additionally, we utilized the Shapley Additive Explanations (SHAP) to interpret the contribution of the EMG features to the regression model for estimation of knee OA severity according to WOMAC and VAS. Muscle activity and co-contraction patterns varied according to the functional limitations associated with knee OA severity according to VAS and WOMAC. The coefficient of determination of the cross-validated regression model is 0.85 for estimating WOMAC, 0.82 for pain, 0.85 for stiffness, and 0.85 for physical function, as well as VAS scores, utilizing the gait features. SHAP explanation revealed that greater co-contraction of lower extremity muscles during the weight acceptance and swing phases indicated more severe knee OA. The identified muscle co-activation patterns may be utilized as objective candidate outcomes to better understand the severity of knee OA.

Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices

BACKGROUND

Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms.

METHODS

This study included 92 participants with variable degrees of KOA. A modified Kellgren-Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost).

RESULTS

Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability.

CONCLUSION

Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans.

Effectiveness of Yijinjing exercise in the treatment of early-stage knee osteoarthritis: a randomized controlled trial protocol

INTRODUCTION

Knee osteoarthritis (KOA) is still a challenging degenerative joint disease with high morbidity and disease burden. Early-stage KOA, the focus of this study, could present a Window of Opportunity to arrest the disease process and reduce the disease burden. Yijinjing exercise is an important part of physical and psychological therapies in Traditional Chinese Exercise and may be an effective treatment. However, there is no clinical efficacy assessment of Yijinjing exercise for patients with early-stage KOA. Therefore, we designed a randomised controlled trial to evaluate the effectiveness of Yijinjing exercise on patients with early-stage KOA.

METHODS AND ANALYSIS

This is a parallel-design, two-arm, analyst assessor-blinded, randomised controlled trial. In total, 60 patients with early-stage KOA will be recruited and randomly assigned to the Yijinjing exercise group (n=30) and health education group (n=30) at a ratio of 1:1, receiving 12 weeks of Yijinjing exercise or health education accordingly. The primary outcome will be measured with the Western Ontario and McMaster Universities Osteoarthritis Index, and the secondary outcomes will include the Visual Analogue Scale, Short-Form 36 Item Health Survey Questionnaire, Beck Depression Inventory, Perceived Stress Scale, Berg Balance Scale, and Gait Analysis for a comprehensive assessment. Outcome measures are collected at baseline, at 12 week ending intervention and at the 12 week, 24 week and 48 week ending follow-up. The primay time point will be 12 weeks postintervention. Adverse events will be recorded for safety assessment.

ETHICS AND DISSEMINATION

This study has been approved by the ethical application of the Shanghai Municipal Hospital of Traditional Chinese Medicine Ethics Committee (2021SHL-KY-78).

TRIAL REGISTRATION NUMBER

ChiCTR2200065178.

Exploring the potential of the sit-to-stand test for self-assessment of physical condition in advanced knee osteoarthritis patients using computer vision

Introduction

Knee osteoarthritis (KOA) is a prevalent condition often associated with a decline in patients' physical function. Objective self-assessment of physical conditions poses challenges for many advanced KOA patients. To address this, we explored the potential of a computer vision method to facilitate home-based physical function self-assessments.

Methods

We developed and validated a simple at-home artificial intelligence approach to recognize joint stiffness levels and physical function in individuals with advanced KOA. One hundred and four knee osteoarthritis (KOA) patients were enrolled, and we employed the WOMAC score to evaluate their physical function and joint stiffness. Subsequently, patients independently recorded videos of five sit-to-stand tests in a home setting. Leveraging the AlphaPose and VideoPose algorithms, we extracted time-series data from these videos, capturing three-dimensional spatiotemporal information reflecting changes in key joint angles over time. To deepen our study, we conducted a quantitative analysis using the discrete wavelet transform (DWT), resulting in two wavelet coefficients: the approximation coefficients (cA) and the detail coefficients (cD).

Results

Our analysis specifically focused on four crucial joint angles: "the right hip," "right knee," "left hip," and "left knee." Qualitative analysis revealed distinctions in the time-series data related to functional limitations and stiffness among patients with varying levels of KOA. In quantitative analysis, we observed variations in the cA among advanced KOA patients with different levels of physical function and joint stiffness. Furthermore, there were no significant differences in the cD between advanced KOA patients, demonstrating different levels of physical function and joint stiffness. It suggests that the primary difference in overall movement patterns lies in the varying degrees of joint stiffness and physical function among advanced KOA patients.

Discussion

Our method, designed to be low-cost and user-friendly, effectively captures spatiotemporal information distinctions among advanced KOA patients with varying stiffness levels and functional limitations utilizing smartphones. This study provides compelling evidence for the potential of our approach in enabling self-assessment of physical condition in individuals with advanced knee osteoarthritis.

Clinical and radiographic characterization of three-dimensional gait profiles of patients with knee osteoarthritis

BACKGROUND

Previous authors have utilized gait kinematics to categorize knee osteoarthritis patients into four distinct profiles: (1) flexed knee; (2) externally rotated knee; (3) stiff knee; and (4) knee varus thrust and rotational rigidity. However, the relationship between these gait profiles and patients' characteristics remains poorly understood. Thus, this study aimed to investigate whether differences in clinical and radiographic characteristics were associated with these four gait profiles.

METHODS

This cross-sectional study used available data from a previous biomechanical study. Data on the four gait profiles were collected from 42 patients with advanced knee osteoarthritis. Three-dimensional kinematics of the knee was recorded during gait using an optoelectronic system. Subjects were evaluated for knee strength, range of motion, tibial slope, femorotibial angle, radiographic severity, anthropometric measurements, and patient-reported outcomes. Multiple comparisons were made using Dunn's test. The level of significance was set at 5%, and the effect size was calculated.

FINDINGS

Body mass index (BMI) was the only variable associated with a specific gait profile: profile 4 (P = 0.01; effect size = P1 × P4: -0.62; P2 × P4: -0.41; P3 × P4: -0.40).

INTERPRETATION

Our findings suggest that most clinical and radiographic characteristics commonly measured in clinical practice did not differ significantly among knee osteoarthritis patients with the four different gait profiles. The only exception was a higher BMI noted in those with gait profile 4; however, it remains unclear whether it can cause varus thrust or rotation rigidity. The incorporation of three-dimensional motion analysis to identify gait profiles provided clinical insights beyond the limitations of traditional clinical assessments.

Comparison of accuracy for hip-knee-ankle (HKA) angle by X-ray and knee motion analysis system and the relationships between HKA and gait posture

BACKGROUND

The lower limb mechanical axis was used to assess the severity of knee osteoarthritis (KOA) with varus/valgus deformity and the accuracy of targeted lower limb alignment correction after operation by conventional X-rays. There are lots of parameters to assess the gait in elder patients such as velocity, stride length, step width and swing/stance ratio by knee joint movement analysis system. However, the correlation between the lower limb mechanical axis and gait parameters is not clear. This study is aimed at obtaining the accuracy of the lower limb mechanical axis by the knee joint movement analysis system and the correlation between the lower limb mechanical axis and gait parameters.

METHODS

We analysed 3D knee kinematics during ground gait of 99 patients with KOA and 80 patients 6 months after the operations with the vivo infrared navigation 3D portable knee joint movement analysis system (Opti-Knee®, Innomotion Inc, Shanghai, China). The HKA (Hip-Knee-Ankle) value was calculated and compared to X-ray findings.

RESULTS

HKA absolute variation after the operation was 0.83 ± 3.76°, which is lower than that before the operation (5.41 ± 6.20°, p = 0.001) and also lower than the entire cohort (3.36 ± 5.72). Throughout the cohort, a significant correlation with low coefficients (r = -0.19, p = 0.01) between HKA value and anterior-posterior displacement was found. In comparing the HKA values measured on the full-length alignment radiographs and 3D knee joint movement analysis system (Opti-Knee), there was a significant correlation with moderate to high coefficients (r = 0.784 to 0.976). The linear correlation analysis showed that there was a significant correlation between the values of HKA measured by X-ray and movement analysis system (R² = 0.90, p < 0.01).

CONCLUSIONS

Data with equivalent results as HKA, the 6DOF of the knee and ground gait data could be provided by infrared navigation based 3D portable knee joint movement analysis system comparing with the conventional X-rays. There is no significant effect of HKA on the kinematics of the partial knee joint.

Radiographic Biomarkers for Knee Osteoarthritis: A Narrative Review

Conventional radiography remains the most widely available imaging modality in clinical practice in knee osteoarthritis. Recent research has been carried out to develop novel radiographic biomarkers to establish the diagnosis and to monitor the progression of the disease. The growing number of publications on this topic over time highlights the necessity of a renewed review. Herein, we propose a narrative review of a selection of original full-text articles describing human studies on radiographic imaging biomarkers used for the prediction of knee osteoarthritis-related outcomes. To achieve this, a PubMed database search was used. A total of 24 studies were obtained and then classified based on three outcomes: (1) prediction of radiographic knee osteoarthritis incidence, (2) knee osteoarthritis progression and (3) knee arthroplasty risk. Results showed that numerous studies have reported the relevance of joint space narrowing score, Kellgren-Lawrence score and trabecular bone texture features as potential bioimaging markers in the prediction of the three outcomes. Performance results of reviewed prediction models were presented in terms of the area under the receiver operating characteristic curves. However, fair and valid comparisons of the models' performance were not possible due to the lack of a unique definition of each of the three outcomes.

Evaluating the effective pressure applied by a valgus knee orthosis in individuals with medial knee osteoarthritis based on the dose-response relationship

BACKGROUND

There is evidence that valgus knee orthosis improves clinical and biomechanical outcomes in individuals with medial knee osteoarthritis (MKOA). It is unclear whether variations in pressure application by orthosis straps can affect the biomechanical outcomes. This study aimed to determine the dose-response relationship between different orthosis straps tensions and changes in knee adduction moment (KAM) parameters in individuals with MKOA.

METHOD

Twenty-four individuals with symptomatic MKOA were enrolled in this quasi-experimental study. Five tension conditions in orthosis straps were tested in 20-mmHg increments, from 0 (no pressure) to 100 (maximal pressure) mmHg. Patients were asked to adjust the orthosis strap tension based on their perceived comfort. After each condition, a 3D gait analysis was performed, and KAM parameters were calculated. The participants also reported their satisfaction with knee orthosis adjustment for each pressure condition.

RESULTS

With successive increases in strap tension from 40 to 80 mmHg, the first peak, second peak, and angular impulse of KAM decreased nonlinearly (from 6 % to 25 %). Increasing the orthosis strap tension to 100 mmHg significantly decreased (P < 0.05) the participants' satisfaction level. The effective dosages (IC₅₀) of pressure for the first peak, second peak, and angular impulse of KAM as responses were 58, 65, and 69 mmHg, respectively.

CONCLUSION

The KAM decline was not linear as the strap pressure increased. Patients were dissatisfied with orthosis adjustment when strap tension was above 80 mmHg. The optimum dosage of pressure on the knee joint's lateral side for adjusting an orthosis' strap tension is approximately 69 mmHg.

Relation of gait measures with mild unilateral knee pain during walking using machine learning

Gait alterations in those with mild unilateral knee pain during walking may provide clues to modifiable alterations that affect progression of knee pain and osteoarthritis (OA). To examine this, we applied machine learning (ML) approaches to gait data from wearable sensors in a large observational knee OA cohort, the Multicenter Osteoarthritis (MOST) study. Participants completed a 20-m walk test wearing sensors on their trunk and ankles. Parameters describing spatiotemporal features of gait and symmetry, variability and complexity were extracted. We used an ensemble ML technique ("super learning") to identify gait variables in our cross-sectional data associated with the presence/absence of unilateral knee pain. We then used logistic regression to determine the association of selected gait variables with odds of mild knee pain. Of 2066 participants (mean age 63.6 [SD: 10.4] years, 56% female), 21.3% had mild unilateral pain while walking. Gait parameters selected in the ML process as influential included step regularity, sample entropy, gait speed, and amplitude dominant frequency, among others. In adjusted cross-sectional analyses, lower levels of step regularity (i.e., greater gait variability) and lower sample entropy(i.e., lower gait complexity) were associated with increased likelihood of unilateral mild pain while walking [aOR 0.80 (0.64-1.00) and aOR 0.79 (0.66-0.95), respectively].

Objective gait assessment in individuals with knee osteoarthritis using inertial sensors: A systematic review and meta-analysis

BACKGROUND

Objective assessment of gait using inertial sensors has shown promising results for functional evaluations in individuals with knee osteoarthritis (OA). However, the large number of possible outcome measures calls for a systematic evaluation of most relevant parameters to be used for scientific and clinical purposes.

AIM

This systematic review and meta-analysis aimed to identify gait parameters derived from inertial sensors that reflect gait deviations in individuals with knee OA compared to healthy control subjects (HC).

METHODS

A systematic search was conducted in five electronic databases (Medline, Embase, Web of Science, CINAHL, IEEE) to identify eligible articles. Risk of bias was assessed using a modified version of the Downs and Black scale. Data regarding study population, experimental procedures, and biomechanical outcomes were extracted. When a gait parameter was reported by a sufficient number of studies, a random-effects meta-analysis was conducted using the inverse variance method.

RESULTS

Twenty-three articles comparing gait between 411 individuals with knee OA and 507 HC were included. Individuals with knee OA had a lower gait speed than HC (standardized mean difference = -1.65), driven by smaller strides with a longer duration. Stride time variability was slightly higher in individuals with knee OA than in HC. Individuals with knee OA walked with a lower range of motion of the knee during the swing phase, less lumbar motion in the coronal plane, and a lower foot strike and toe-off angle compared to HC.

SIGNIFICANCE

This review shows that inertial sensors can detect gait impairments in individuals with knee OA. Large standardized mean differences found on spatiotemporal parameters support their applicability as sensitive endpoints for mobility in individuals with knee OA. More advanced measures, including kinematics of knee and trunk, may reveal gait adaptations that are more specific to knee OA, but compelling evidence was lacking.

Joint Torque Prediction via Hybrid Neuromusculoskeletal Modelling during Gait Using Statistical Ground Reaction Estimates: An Exploratory Study

Joint torques of lower extremity are important clinical indicators of gait capability. This parameter can be quantified via hybrid neuromusculoskeletal modelling that combines electromyography-driven modelling and static optimisation. The simulations rely on kinematics and external force measurements, for example, ground reaction forces (GRF) and the corresponding centres of pressure (COP), which are conventionally acquired using force plates. This bulky equipment, however, hinders gait analysis in real-world environments. While this portability issue could potentially be solved by estimating the parameters through machine learning, the effect of the estimation errors on joint torque prediction with biomechanical models remains to be investigated. This study first estimated GRF and COP through feedforward artificial neural networks, and then leveraged them to predict lower-limb sagittal joint torques via (i) inverse dynamics and (ii) hybrid modelling. The approach was evaluated on five healthy subjects, individually. The predicted torques were validated with the measured torques, showing that hip was the most sensitive whereas ankle was the most resistive to the GRF/COP estimates for both models, with average metrics values being 0.70 < R2 < 0.97 and 0.069 < RMSE < 0.15 (Nm/kg). This study demonstrated the feasibility of torque prediction based on personalised (neuro)musculoskeletal modelling using statistical ground reaction estimates, thus providing insights into potential real-world mobile joint torque quantification.

Are there different gait profiles in patients with advanced knee osteoarthritis? A machine learning approach

BACKGROUND

Determine whether knee kinematics features analyzed using machine-learning algorithms can identify different gait profiles in knee OA patients.

METHODS

3D gait kinematic data were recorded from 42 patients (Kellgren-Lawrence stages III and IV) walking barefoot at individual maximal gait speed (0.98 ± 0.34 m/s). Principal component analysis, self-organizing maps, and k-means were applied to the data to identify the most relevant and discriminative knee kinematic features and to identify gait profiles.

FINDINGS

Four different gait profiles were identified and clinically characterized as type 1: gait with the knee in excessive varus and flexion (n = 6, 14%, increased knee adduction and increased maximum and minimum knee flexion, p < 0.01); type 2: gait with knee external rotation, either in varus or valgus (n = 11, 26%, excessive maximum and minimum external rotation, p < 0.001); type 3: gait with a stiff knee (n = 17, 40%, decreased knee flexion range of motion, p < 0.001); and type 4: gait with knee varus 'thrust' and decreased rotation (n = 8, 19%, increased and reduced range of motion in the coronal and transverse plane, respectively, p < 0.05).

INTERPRETATION

In a group of patients with homogeneous Kellgren-Lawrence classification of knee OA, gait kinematics data permitted to identify four different gait profiles. These gait profiles can be a valuable tool for helping surgical decisions and treatment. To allow generalization, further studies should be carried with a larger and heterogeneous population.

Self-Perception of the Knee Is Associated with Joint Motion during the Loading Response in Individuals with Knee Osteoarthritis: A Pilot Cross-Sectional Study

Small knee flexion motion is a characteristic of gait in individuals with knee osteoarthritis. This study examined the relationship between knee flexion excursion in loading response and knee self-perception in individuals with knee osteoarthritis. Twenty-one individuals with knee osteoarthritis participated in this study. Knee flexion excursions in loading response while walking at a comfortable and a fast-walking speed were measured using an inertial measurement unit-based motion capture system. The degree of knee perceptual impairment was evaluated using the Fremantle Knee Awareness Questionnaire (FreKAQ). The relationships between the FreKAQ score and gait variables and knee function were evaluated by calculating the correlation coefficient. The unique contributions of knee self-perception and muscle strength to knee flexion excursion in loading response were analyzed using hierarchical linear regression. Knee self-perception was significantly correlated with pain during walking, muscle strength and knee flexion excursion at fast speed. In the fast speed condition only, impaired knee self-perception was inversely proportional to knee flexion excursion and accounted for 21.8% of the variance in knee flexion excursion. This result suggests that impaired self-perception of the knee may help to explain the decrease in the knee flexion excursion in the loading response in individuals with knee osteoarthritis.

A machine learning-based diagnostic model associated with knee osteoarthritis severity

Knee osteoarthritis (KOA) is characterized by pain and decreased gait function. We aimed to find KOA-related gait features based on patient reported outcome measures (PROMs) and develop regression models using machine learning algorithms to estimate KOA severity. The study included 375 volunteers with variable KOA grades. The severity of KOA was determined using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). WOMAC scores were used to classify disease severity into three groups. A total of 1087 features were extracted from the gait data. An ANOVA and student's t-test were performed and only features that were significant were selected for inclusion in the machine learning algorithm. Three WOMAC subscales (physical function, pain and stiffness) were further divided into three classes. An ANOVA was performed to determine which selected features were significantly related to the subscales. Both linear regression models and a random forest regression was used to estimate patient the WOMAC scores. Forty-three features were selected based on ANOVA and student's t-test results. The following number of features were selected from each joint: 12 from hip, 1 feature from pelvic, 17 features from knee, 9 features from ankle, 1 feature from foot, and 3 features from spatiotemporal parameters. A significance level of < 0.0001 and < 0.00003 was set for the ANOVA and t-test, respectively. The physical function, pain, and stiffness subscales were related to 41, 10, and 16 features, respectively. Linear regression models showed a correlation of 0.723 and the machine learning algorithm showed a correlation of 0.741. The severity of KOA was predicted by gait analysis features, which were incorporated to develop an objective estimation model for KOA severity. The identified features may serve as a tool to guide rehabilitation and progress assessments. In addition, the estimation model presented here suggests an approach for clinical application of gait analysis data for KOA evaluation.

Relationship between Quadriceps Tendon Young's Modulus and Maximum Knee Flexion Angle in the Swing Phase of Gait in Patients with Severe Knee Osteoarthritis

Background and objectives: Decreased knee flexion in the swing phase of gait can be one of the causes of falls in severe knee osteoarthritis (OA). The quadriceps tendon is one of the causes of knee flexion limitation; however, it is unclear whether the stiffness of the quadriceps tendon affects the maximum knee flexion angle in the swing phase. The purpose of this study was to clarify the relationship between quadriceps tendon stiffness and maximum knee flexion angle in the swing phase of gait in patients with severe knee OA. Materials and Methods: This study was conducted from August 2018 to January 2020. Thirty patients with severe knee OA (median age 75.0 (interquartile range 67.5–76.0) years, Kellgren–Lawrence grade: 3 or 4) were evaluated. Quadriceps tendon stiffness was measured using Young’s modulus by ShearWave Elastography. The measurements were taken with the patient in the supine position with the knee bent at 60° in a relaxed state. A three-dimensional motion analysis system measured the maximum knee flexion angle in the swing phase. The measurements were taken at a self-selected gait speed. The motion analysis system also measured gait speed, step length, and cadence. Multiple regression analysis by the stepwise method was performed with maximum knee flexion angle in the swing phase as the dependent variable. Results: Multiple regression analysis identified quadriceps tendon Young’s modulus (standardized partial regression coefficients [β] = −0.410; p = 0.013) and gait speed (β = 0.433; p = 0.009) as independent variables for maximum knee flexion angle in the swing phase (adjusted coefficient of determination = 0.509; p < 0.001). Conclusions: Quadriceps tendon Young’s modulus is a predictor of the maximum knee flexion angle. Clinically, decreasing Young’s modulus may help to increase the maximum knee flexion angle in the swing phase in those with severe knee OA.