Comparative Analysis of Ensemble Models in Machine Learning for Human Activity Recognition in Wearable Health Systems

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Ali Reza; Kanani Viuona

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Seminar Paper

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Year:2025 | Issue:11

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Title

Comparative Analysis of Ensemble Models in Machine Learning for Human Activity Recognition in Wearable Health Systems

Author(s)

Ali Reza | Kanani Viuona | Issue Writer Certificate

Keywords

Human Activity Recognition

Wearable Sensors

Machine Learning

Personalized Health Monitoring

Random Forest

XGBoost

Activity Classification

Class Imbalance

Real-time Monitoring

Abstract

Wearable technologies have become a crucial part of modern health monitoring systems, offering insights into physical activities and physiological states through sensors such as accelerometers, gyroscopes, and ECGs. Activity recognition, the task of identifying and classifying physical activities from sensor data, is central to applications like fitness tracking, fall detection, and chronic disease management. In this study, we compare the performance of two widely used Machine Learning algorithms, Random Forest and XGBoost, for activity recognition using the MHEALTH dataset, which records data from multiple Wearable Sensors across 12 distinct activities. Our results show that Random Forest, with class weight adjustments, outperforms XGBoost, achieving an accuracy of 94. 72%, while XGBoost achieved 93. 04%. This study highlights the importance of Class Imbalance handling and contributes to improving real-time health monitoring systems by demonstrating the effectiveness of class-weight adjustment in ensemble models for Human Activity Recognition.

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