Structured Representation Learning of Multi-Class Gait Dynamics from Wearable Sensor-Derived Motion Sequences

Abstract

The widespread use of wearable sensor technologies has led to an exponential increase in continuously generated motion data, creating new opportunities for automated human activity recognition in areas such as healthcare monitoring, fitness tracking, assisted living, and smart environments. Conventional approaches that rely on manual observation or rule-based classification with predefined thresholds are often inadequate for handling complex activity patterns, sensor noise, and high-dimensional data streams, resulting in reduced accuracy and limited generalization capabilities. A major challenge in this domain is the reliable interpretation of continuous, multidimensional sensor data under dynamic conditions, including variations in user behavior, device orientation, and sensor placement. To address these limitations, this study proposes a robust and scalable machine learning-based framework for activity classification that leverages multiple algorithms, including Greedy Tree (GT), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naïve Bayes (NB), and Adaptive Boosting (AB). The system is designed as an end-to-end pipeline incorporating essential stages such as data cleaning, normalization, feature scaling, model training, performance evaluation, and prediction. By systematically comparing different models, the results demonstrate that the Greedy Tree classifier significantly outperforms other techniques, achieving an accuracy of 99.00% on the target activity variable, while KNN, NB, LR, and AB achieve comparatively lower accuracies of 77.85%, 57.40%, 51.10%, and 51.02%, respectively. This indicates the superior capability of tree-based models in capturing complex patterns and decision boundaries within sensor data. Overall, the proposed framework enhances classification accuracy, improves robustness against noisy and variable data, and ensures scalability for real-time as well as batch processing, making it highly suitable for deployment in modern intelligent monitoring systems.