Adaptive Latent Motion Intelligence for Multi-Gait Behavioral Understanding in Sensor-Embedded Wearable Ecosystems

Abstract

Abstract

Human activity recognition based on wearable sensor data has become increasingly important due to its applications in areas such as healthcare, fitness tracking, and intelligent environments. The widespread use of sensor-equipped devices has led to the generation of large volumes of continuous motion data, making efficient analysis methods essential. Earlier approaches to activity recognition primarily depended on manual observation or rule-based techniques that relied on predefined thresholds. However, these methods struggled to capture complex activity patterns, handle noisy signals, and scale effectively to large datasets, often resulting in limited accuracy and poor generalization. A major challenge in this domain is the accurate classification of activities from highdimensional, continuously streaming sensor data while maintaining scalability and reliability. Conventional techniques lack flexibility and tend to perform poorly when faced with variations such as changes in user behavior or differences in sensor positioning. This highlights the need for intelligent, data-driven systems capable of automatically learning meaningful patterns. To overcome these limitations, this study proposes a machine learning-based framework for activity classification that employs multiple models, including Greedy Tree (GT), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), and Adaptive Boosting (AB). The system incorporates data preprocessing, feature normalization, model training, evaluation, and prediction within an integrated pipeline. Experimental findings reveal that the Greedy Tree (GT) model achieves the highest performance, with an accuracy of 99.00% on the “activity” target variable, surpassing KNN (77.85%), NB (57.40%), LR (51.10%), and AB (51.02%). Overall, the proposed approach enhances classification accuracy, robustness, and scalability compared to traditional methods. It effectively manages continuous sensor data and supports both real-time and offline prediction scenarios.