Optimizing Machine Learning Models in Human Activity Recognition with the WISDM Dataset

Abstract

Human Activity Recognition (HAR) is a technology with significant potential, utilizing data from various devices like smartphones and cameras. It finds applications in daily activities such as driving, cleaning, and gaming, involving fundamental movements like standing, sitting, jogging, and typing. Accurate identification of these actions is crucial for effective human-computer interaction systems. This work incorporates a HAR module to extract valuable insights from data signals. It employs Machine Learning (ML) models to automatically detect human activities using raw data from Internet of Things wearable sensors, including innovative combinations like Adagrad and ELU within the MLP algorithm. The ML models' performance is evaluated using statistical metrics such as accuracy, precision, recall, and f1-score, and comparisons are made with existing models.