Finally, the outcome of testing both healthy individuals and MS patients showed that the system had an average accuracy of 87.5% and 82.5%, respectively, when it came to identifying abnormalities in such diseases with varying progress rates among patients. However, there were a few misclassifications caused by the small sample size and slight variations in gait patterns, although the overall system performance was satisfactory. In another study [24], a novel automatic gait anomaly detection method called AGD-SSC was introduced, which works online to differentiate between normal and abnormal gaits of individuals. Gait features were extracted by calculating F-GEI (cumulative energy normalized across walking cycles), and the BC-COP-K-means clustering algorithm along with a boundary clamping mechanism were employed for classification. The proposedmethod was evaluated in experiments using a human gait dataset, comparing the results to IF, LOF, and direct COP-K-means. The outcome indicated usage possibilities in situations where the silhouette distortion caused by apparel or shoe type was minimal, including large grazing animals' abnormal gait. [PERSON] and [PERSON] [25] sought to determine if gait analysis through ground reaction forces (GRFs) could be of use in any way to medical practice and sports. The GaitRec and Gutenberg databases were employed using machine learning (ML) techniques to investigate gait patterns in patients with different disorders as compared to healthy controls. GRF signals were pre-processed, several feature extraction methods were employed, and the selection of features was also undertaken. Using the K-Nearest Neighbors (KNN) algorithm appeared to enable better performance. Several classification schemes were used in the study, which found that three-dimensional GRF data improved general performance. Also, time-domain and wavelet features were identified as valuable for distinguishing between different gait patterns. The results showed a potential step forward towards automating the diagnosis of locomotor dysfunction. The investigation into human gait activity recognition, as documented in the National Institutes of Health's PMC database, involved the comparison of various algorithms for the classification of gait patterns. Notably, a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) achieved exceptional accuracy levels, reaching approximately 99%. Surprisingly, simpler models such as the Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) algorithms also demonstrated noteworthy performance, surpassing 94% accuracy. This underscores the pivotal trade-off between model complexity and performance, emphasizing that while complex models may not always be requisite, the selection of an appropriate simpler model remains paramount. The research on gait pattern recognition for fall prediction delved into the utilization of machine learning techniques for predicting falls through gait analysis [26]. The findings revealed that the Support Vector Machine (SVM) algorithm exhibited the highest accuracy, approximately 95%, in distinguishing various gait patterns compared to the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models. However, a notable limitation identified was the utilization of a relatively small dataset comprising 750 gait samples. To enhance the robustness of gait detection and fall prediction, larger and more diverse datasets are imperative. A study explored the application of machine learning algorithms for the early detection of [PERSON]'s disease via gait analysis [27]. Although the results exhibited promise, the authors acknowledged the necessity for further investigation into factors such as clothing variations and walking surfaces, which could potentially influence the accuracy of gait analysis. This underscores the ongoing need for comprehensive research to refine and optimize machine learning-based approaches in gait analysis for [PERSON]'s disease detection.