Automated Human Activity Classification

Abstract

Human activity recognition (HAR) has emerged as a significant research domain within artificial intelligence (AI) and machine learning (ML), focusing on the automatic detection of human movements such as walking, running, sitting, and lying down. This study explores the design of an HAR model utilizing data captured from smartphone sensors, specifically the accelerometer and gyroscope. The methodology involves stages such as data preprocessing, feature extraction, and classification using both conventional ML techniques and deep learning approaches. Experimental results indicate that deep neural networks deliver superior performance compared to models like support vector machines (SVM) and random forests, particularly in terms of accuracy and reliability. These outcomes highlight the practical value of HAR systems in areas including healthcare monitoring, personalized services, and advanced human–computer interaction.

In recent years, HAR systems have gained importance in applications such as patient rehabilitation, fall detection in elderly individuals, remote health supervision, and fitness tracking. The combination of sensor technology and AI algorithms allows continuous monitoring without human intervention, enabling context-aware decision-making in real time. Moreover, integrating deep learning models such as convolutional neural networks (CNN) and long short-term memory (LSTM) networks enhances system adaptability to user-specific patterns and varying environments. This study presents a comprehensive approach combining traditional ML and deep learning models for efficient human activity classification. The findings underscore the potential of HAR to revolutionize smart healthcare, IoT-based automation, and intelligent wearable technologies.