Gait kinematics refers to the study of human walking patterns and movements, encompassing the detailed analysis of joint angles, segmental movements, and overall body posture during the gait cycle. As a specialized field within biomechanics, gait kinematics emphasizes the quantitative assessment of how individuals walk, providing critical insights into the mechanics of human locomotion. This analysis is particularly significant in understanding the impact of osteoarthritis on mobility and functional performance.

One of the key components of gait kinematics is joint angle measurement, which involves quantifying the angles at which various joints move during walking. In individuals with osteoarthritis, alterations in joint angles are commonly observed, especially in weight-bearing joints such as the knees and hips. These changes can indicate compensatory strategies that individuals adopt to manage pain and maintain mobility.

In addition to joint angles, various gait parameters can be analyzed, including stride length, step width, and walking speed. Osteoarthritis often leads to modifications in these parameters as individuals adjust their walking patterns to minimize discomfort. For instance, a person with osteoarthritis may shorten their stride or alter their step width to relieve pressure on affected joints. These adaptations are essential to monitor, as they can provide valuable information about the severity of the condition and the effectiveness of therapeutic interventions.

Joint loading patterns are another crucial aspect of gait analysis. This component examines how weight is distributed across joints during movement, revealing the potential for altered loading in individuals with osteoarthritis. For instance, affected individuals may unconsciously shift their weight away from the painful joint, leading to imbalances that can further exacerbate mobility issues. Analyzing these loading patterns can help healthcare professionals develop tailored rehabilitation strategies.

The assessment of asymmetry in gait is also vital, as osteoarthritis can lead to noticeable differences in movement between legs. For example, if one knee is more affected by osteoarthritis, the individual may exhibit a distinct asymmetry in their walking pattern, which can contribute to increased strain on other joints. Recognizing these asymmetries is essential for implementing effective treatment plans that address the underlying issues.

Temporal spatial parameters, which involve measuring time intervals and distances during walking, further enrich the understanding of gait kinematics. Changes in these parameters may occur as individuals with osteoarthritis adapt their movements to reduce pain. By analyzing these factors, clinicians can gain insight into how the disease affects mobility and identify appropriate interventions.

Moreover, pain assessment plays a crucial role in gait analysis. By combining objective gait measurements with self-reported pain levels, clinicians can develop a comprehensive understanding of how osteoarthritis influences an individual’s walking pattern. This multifaceted approach enhances the ability to address the patient’s needs effectively.

Longitudinal monitoring of gait kinematics is invaluable for tracking changes over time. This method allows for the assessment of osteoarthritis progression and the evaluation of the effectiveness of various interventions. By routinely measuring gait parameters, healthcare providers can make informed decisions regarding treatment adjustments.

Finally, gait kinematics offers objective assessment tools that provide quantifiable data on how osteoarthritis impacts mobility. This objective measurement can complement subjective evaluations, leading to better treatment planning and patient outcomes.

Several medical research laboratories have developed databases to facilitate research on conditions like knee osteoarthritis (KOA) and Parkinson’s disease (PD). One such dataset is the KOA-PD-NM (Kour et al., 2020), which we have utilized in our study (Figure 1). This dataset accounts for key variables such as age, gender, and disease severity, providing a robust foundation for analyzing both normal (NM) and abnormal (KOA, PD) subjects. Its unique contribution lies in enabling the comprehensive evaluation of not only lower body movements but also upper body dynamics, including arm movements and posture.

In our study, we leverage the KOA-PD-NM dataset to compare gait deviations between patients and healthy individuals. By doing so, we aim to enhance the understanding of disease progression and contribute to the development of more refined diagnostic techniques and strategies.

The KOA-PD-NM Gait Video Dataset (Kour et al., 2020) consists of 96 subjects: 50 with KOA, 16 with PD, and 30 NM subjects (Table 1). Each individual is represented by two sequences left-to-right and right-to-left captured in the frontal and sagittal planes. The dataset includes MOV format videos, recorded using a NIKON DSLR 5,300 camera positioned 8 m away from a walking mat in a clinical setting. While the original dataset involved the use of six red passive reflective markers on the body joints, our study takes a different approach by detecting gait abnormalities without relying on these markers, aiming for a more accessible and markerless diagnostic method.

Several advanced studies are available for assessing and quantifying gait patterns, providing a quantitative measure of ambulatory activity (Balti et al., 2022). These technologies facilitate gait analysis by extracting a variety of parameters, including kinematic, kinetic, electrical signal, and spatiotemporal variables, which contribute to a comprehensive understanding of gait characteristics (Salchow et al., 2022). For instance, some approaches utilize energy images for human gait analysis, while others employ a bottom-up pose estimation system that leverages a 3D markerless multimodal motion capture framework (Bakchy et al., 2022; Viswakumar et al., 2022).

In this study, we concentrate on classifying human gaits based on a streamlined set of parameters extracted from RGB videos. Recent advancements in technology have introduced several innovative libraries designed for human pose extraction and body joint estimation. After extensive research and experimentation, we selected MediaPipe (Kim et al., 2023), MediaPipe is a powerful python library that employs state-of-the-art machine learning techniques for precise body pose tracking. MediaPipe can extract 33 (3D) landmarks through a bottom-up approach, effectively capturing the critical joints and points along the body. The Figure 2 illustrate the extracted body landmarks, showcasing their importance in accurately analyzing and classifying gait patterns.

The methodology begins with the collection of a video dataset that includes several categories: NM (normal), KOA_EL (early-stage knee osteoarthritis), KOA_MD (mid-stage), and KOA_SV (severe-stage). From these videos, key kinematic features such as joint angles and distances are extracted to analyze the motion of joints and body segments during gait. The data is then cleaned through filtering and reduction processes to ensure accuracy and relevance. Afterward, the numerical dataset is split into two portions, one for training and validation and the other for testing. The machine learning model is trained and optimized using the training set, while the test set is employed to evaluate its performance. Finally, the model’s accuracy is calculated, determining how effectively it can classify different KOA stages based on the gait data. This structured approach enables a reliable and comprehensive system for KOA classification (Figure 3).

Throughout each phase of the gait cycle, various kinematic parameters are used to represent and analyze human walking patterns (Rodrigues et al., 2020). Machine learning techniques are applied to process and analyze the data, offering a precise method for assessing human gait. The specific kinematic features selected for this project are detailed in Table 2.

Below is a full explanation of the calculations and key parameters, accompanied by illustrative figures that depict the precise positioning of angles, heel and steps. These visualisations include example curves that effectively demonstrate the dynamic patterns of gait, providing a deeper insight into the biomechanical characteristics essential to the analysis.

Gait symmetry refers to the degree of similarity between the movements of the left and right limbs during activities like walking or running. It is a critical measure in biomechanics, as it quantifies how closely the movement patterns of one side of the body mirror those of the opposite side. This concept is particularly important for assessing balance and coordination, as well as identifying potential movement impairments.

To assess gait symmetry, various kinematic parameters such as joint angles, range of motion, and timing of limb movements are analyzed. Discrepancies in these parameters between the left and right sides can reveal underlying imbalances or irregularities. For example, asymmetry in joint angles or step length may indicate musculoskeletal disorders, neurological conditions, or compensatory patterns due to pain or injury. Accurate measurement of gait symmetry helps clinicians and researchers identify movement inefficiencies, diagnose conditions, and tailor rehabilitation programs to restore normal and balanced gait patterns.

The assessment of gait symmetry involves the utilization of various comparative techniques. In our study, we chose to use Pearson correlation coefficient noted “r” (Equation 8).

The Pearson correlation coefficient is often denoted as r and is widely used in statistics and data analysis to explore relationships between variables.

The Pearson correlation coefficient ranges from −1 to 1. 1 indicates that two random variables are perfectly positively correlated, −1 indicates that two random variables are perfectly negatively correlated, and 0 indicates that two random variables are not correlated.

The formula for calculating the Pearson correlation coefficient between two variables, X and Y, with n data points is as follows: r = ∑ i − 1 n X i − X ¯ X i − Y ¯ ∑ i − 1 n X i − X ¯ 2 ∑ i − 1 n Y i − Y ¯ 2 (8)

Where:

X i and Y i are the individual data points for variables X and Y, respectively.

X ¯ and Y ¯ are the mean values of variables X and Y, respectively.

Figure 10 illustrates the correlation between the left and right knee angles of a normal individual and a person diagnosed with knee osteoarthritis (KOA). The blue points represent the Pearson correlation coefficients for the normal group (NM), indicating a healthy and symmetrical movement pattern. In contrast, the red points represent the Pearson correlation coefficients for the KOA_SV group, which corresponds to individuals in the severe stage of knee osteoarthritis. This visual comparison highlights the differences in knee angle relationships between the two groups, emphasizing how the severity of osteoarthritis impacts gait symmetry and joint function.

We conducted our experiments utilizing the gait dataset to analyze the severity of knee osteoarthritis and Parkinson’s disease (Bishop et al., 2016). This comprehensive dataset encompasses gait data from both healthy individuals and those diagnosed with knee osteoarthritis (KOA), providing a valuable resource for our analysis.

For our study, we specifically focused on a subset of the dataset that included 30 video recordings from normal, healthy subjects. Each subject was recorded performing two sequences of gait, one from left to right and the other from right to left, all captured in the frontal and the sagittal plane. In addition, the dataset features 50 video files that document various stages of knee osteoarthritis, categorized into three distinct severity levels: early, moderate and severe. This structured approach allows for a nuanced understanding of how gait is affected across different stages of KOA.

To classify human gait patterns effectively, we employed three different machine learning algorithms: logistic regression, random forest (RF), and support vector machine (SVM). The primary aim of this classification was to differentiate between healthy individuals and those with varying grades of knee osteoarthritis based on their gait characteristics.

Following the training of our models, the dataset was divided into training and validation sets, with 20% reserved for testing the algorithms’ performance. To enhance the robustness of our findings, we utilized cross-validation techniques alongside the scikit-learn library for our analysis. The results of this evaluation, including performance metrics for each model, are summarized in Table 3, offering insights into the efficacy of each classification approach in distinguishing between the two groups.

Following a comprehensive analysis of the training process using this limited dataset, we found that the random forest model exhibited a remarkable accuracy of 96.9%, significantly outperforming the other algorithms. In contrast, the logistic regression model achieved an accuracy of 94%, while the support vector machine (SVM) model lagged behind at 88%.

These results highlight the random forest model’s robustness and effectiveness in classifying gait patterns associated with knee osteoarthritis. However, there is a clear opportunity for improvement with the other models. By expanding the dataset to include a greater variety of samples and potentially more diverse gait patterns, we anticipate that both the logistic regression and SVM models will demonstrate enhanced performance. This expansion could lead to more reliable classifications and a better understanding of the subtle gait variations present in individuals with varying degrees of knee osteoarthritis.

Figure 11 highlights the performance of the SVM model through its learning curve, validation curve, and confusion matrix. The learning curve indicates a steady increase in accuracy as training progresses, ultimately exceeding 85%. This suggests that the model’s performance could further improve with additional training data. The validation curve shows optimal performance at a C value of 10^1, striking a balance between underfitting and overfitting, with accuracy peaking around 90%. The confusion matrix reveals the model’s high accuracy in predicting healthy individuals, with only 1 misclassification. However, it struggles slightly with predicting unhealthy cases, incorrectly classifying 3 out of 12 as healthy. This indicates room for refinement in handling complex cases.

Figure 12 illustrates the learning curve, validation curve, and confusion matrix for the Random Forest classifier. The learning curve demonstrates a consistent improvement in accuracy as the training set size increases, stabilizing at 94% after reaching 80 training examples. The validation curve reveals that accuracy initially improves as the regularization parameter C increases, but after a certain threshold, further increases cause a decline in performance. The optimal C value is around 10^−1. The confusion matrix showcases the model’s effectiveness, with only 2 false negatives and no false positives, highlighting its strong capacity to distinguish between healthy and unhealthy individuals. This shows robust classification, though additional fine-tuning could potentially improve its performance even further.

Figure 13 presents the performance of the Random Forest model via its learning curve, validation curve, and confusion matrix. The learning curve shows that accuracy steadily improves, stabilizing after 60 training examples and reaching 86%. The validation curve demonstrates that high accuracy is achieved quickly with a small number of estimators (trees), leveling off around 20 estimators, with diminishing returns beyond that point. The confusion matrix confirms the model’s strong performance, with 19 true negatives, 12 true positives, 1 false positive, and no false negatives. This indicates a highly accurate model with only one misclassification, emphasizing its robustness in both identifying healthy and unhealthy cases.

The Random Forest model likely outperformed both Logistic Regression and SVM due to its ability to capture non-linear relationships and complex interactions between features. This capability allows it to excel even with smaller datasets by generating diverse models that generalize better. In contrast, Logistic Regression and SVM are more prone to overfitting or failing to recognize complex patterns without sufficient data. The Random Forest’s inherent capacity to handle feature complexity and variability makes it a more reliable choice in this scenario.

In this experiment, the primary objective was to classify individuals based on the severity of knee osteoarthritis (KOA), with a focus on evaluating the performance of two distinct classification methods: Support Vector Machine (SVMs) and Logistic Regression. The aim was to determine how effectively these models could differentiate between varying levels of KOA severity. A comprehensive comparison (Table 4) was conducted by analyzing key performance metrics, including accuracy, precision, and recall. By assessing these metrics, we sought to gauge the models’ ability not only to correctly categorize individuals but also to minimize false positives and false negatives, thus providing a more precise evaluation of their reliability in identifying KOA severity stages. This comparison is essential for identifying the most suitable model for clinical applications, where accurate classification is critical for effective diagnosis and treatment planning.

However, the accuracy achieved in this experiment may not be sufficient. One limitation is the relatively small dataset used likely contributed to overfitting, The limited sample size likely contributed to the reduced performance of both classification methods.

We conducted a comprehensive comparison between the proposed knee osteoarthritis (KOA) and normal gait pattern classification methodology and the state-of-art methodologies, with a primary focus on the accuracy parameter. Table 5 provides a detailed comparison of the accuracy between our proposed method and other leading techniques for knee osteoarthritis (KOA) classification. Yang et al. (2020) introduced a sensor-based approach using Support Vector Machine (SVM), which achieved an accuracy of 92.8%. However, their method is reliant on a complex hardware setup, including a PC and wireless IMU sensors, and requires expert handling for software operation, making it suitable only for highly equipped laboratories. Kour et al. (2020) employed a vision-based method with marker points and K-Nearest Neighbors (KNN), achieving a higher accuracy of 95.16%. Nonetheless, their approach necessitates a controlled lab environment and requires considerable space and time to accurately capture data, limiting its practicality in real-world settings. In contrast, our proposed method, which is both markerless and vision-based, utilizes a Random Forest classifier and achieves an accuracy of 96.9%. This approach not only surpasses the accuracy of previous methods but also offers the advantage of being non-invasive and more adaptable to diverse environments. However, further improvements in performance can be achieved with access to larger datasets for training and refinement.