A Comparative Analysis of XGBoost and LightGBM Approaches for Human Activity Recognition: Speed and Accuracy Evaluation

Abstract

Human activity recognition is the process of automatically identifying and classifying human activities based on data collected from different modalities such as wearable sensors, smartphones, or similar devices having necessary sensors or cameras capturing the behavior of the individuals. In this study, XGBoost and LightGBM approaches for human activity recognition are proposed and the performance and execution times of the proposed approaches are compared. The proposed methods on a dataset including accelerometer and gyroscope data acquired using a smartphone for six activities. The activities are laying, sitting, standing, walking, walking downstairs, and walking upstairs. The available dataset is divided into training and test sets, and proposed methods are trained using the training set, and tested on the test sets. At the end of the study, 97.23% accuracy using the LightGBM approach, and 96.67% accuracy using XGBoost is achieved. It is also found that XGBoost is faster than the LightGBM, whenever the execution times are compared. © IJCESEN.