Human Activity Recognition Using Smartphone Sensors Based on XGBoost Model

Ruikang Hu

2024

Abstract

The core viewpoint of this study focuses on Human Activity Recognition (HAR) through machine learning techniques and utilizing the large amount of data brought by smartphone sensors. The increasing integration of smartphones into daily life emphasizes the need for cost-effective and convenient solutions for HAR. The goal is to explore the potential and performance of smartphone sensors in recognizing diverse activities and distinguishing between different users. This study first considers using Principal Component Analysis (PCA) as a feature dimensionality reduction and visualization analysis tool. Secondly, t-distributed Stochastic Neighbors Embedding (t-SNE) is introduced for further analysis and discussion. This paper introduces an XGBoost model for classification and contrasts it with various models. The unique feature of the XGBoost model lies in its ability to handle complex non-linear relationships, possessing high interpretability and robustness. It integrates multiple weak learners and continuously optimizes model performance through gradient boosting techniques, showcasing excellent performance in classification tasks. The experiments demonstrate high accuracy in recognizing basic activities, reaching up to 97.18%. When identifying a variety of intense sports activities, the accuracy remains high at 92.15%. In distinguishing between different users, the accuracy peaks at 93.27% for specific activities, and accurate recognition of human motion states can be achieved in less than one and a half minutes. Results highlight the feasibility of replacing traditional motion sensors with smartphone sensors, emphasizing practical applications in healthcare, fitness guidance, and gaming.

Paper Citation

in Harvard Style

Hu R. (2024). Human Activity Recognition Using Smartphone Sensors Based on XGBoost Model. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 286-292. DOI: 10.5220/0012824400004547

in Bibtex Style

@conference{icdse24,
author={Ruikang Hu},
title={Human Activity Recognition Using Smartphone Sensors Based on XGBoost Model},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={286-292},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012824400004547},
isbn={978-989-758-690-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Human Activity Recognition Using Smartphone Sensors Based on XGBoost Model
SN - 978-989-758-690-3
AU - Hu R.
PY - 2024
SP - 286
EP - 292
DO - 10.5220/0012824400004547
PB - SciTePress