Analysis of Machine Learning Models for Autonomy in Smart Environments

• Grant number: 24/23235-8
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: July 01, 2025
• End date: June 30, 2026
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Caetano Mazzoni Ranieri
• Grantee: Laura Sthefany Colombo
• Host Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil

Abstract

The advancement of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has driven the development of solutions aimed at improving the quality of life of vulnerable populations, such as the elderly and people with reduced mobility, through assisted smart environments. In these applications, human activity recognition is essential for context inference, leading to developments in feature extraction techniques and machine learning methods. This project proposes a comparative analysis of approaches for human activity recognition based on data from inertial units and sensors integrated into the environment, including multimodal approaches. The focus will be on classical machine learning techniques, such as decision trees and Random Forest, as well as the application of statistical methods for time series modeling and basic signal processing. Experiments will be conducted using widely disseminated public datasets, in order to evaluate performance in terms of accuracy and computational efficiency. With this project, it is expected to provide a framework for the development of new projects involving techniques for human activity recognition in smart environments.