Enhancing human activity recognition with machine learning: insights from smartphone accelerometer and magnetometer data.

ABSTRACT: The domain of Human Activity Recognition (HAR) has undergone a remarkable evolution, driven by advancements in sensor technology, artificial intelligence (AI), and machine learning algorithms. The aim of this article consists of taking as a basis the previously obtained results to implement other techniques to analyze the same dataset and improve the results previously obtained in the different studies, such as neural networks with different configurations, random forest, support vector machine, CN2 rule inducer, Naive Bayes, and AdaBoost. The methodology consists of data collection from smartphone sensors, data cleaning and normalization, feature extraction techniques, and the implementation of various machine learning models. The study analyzed machine learning models for recognizing hum

SUBMITTER: Silva Zendron LA 

PROVIDER: S-EPMC12453735 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature