Human Activity Recognition using machine learning

Abstract

The project on "Human Activity Recognition" explores the implementation of sensor fusion and machine learning techniques to accurately identify and classify human activities in various scenarios. This research aims to contribute to the development of intelligent systems capable of understanding and responding to human behavior. The project focuses on utilizing data from diverse sensors, including accelerometers and gyroscopes, to capture intricate patterns associated with activities such as walking, running, sitting, and other movements.

The proposed system employs advanced machine learning algorithms for the analysis of sensor data, aiming to enhance the accuracy and efficiency of human activity recognition. By integrating multiple sensor modalities, the project aims to create a robust model capable of handling diverse environmental conditions and activity contexts. The research explores the

application of deep learning methodologies to extract meaningful features and patterns from the sensor data, enabling real-time and context-aware activity recognition.

The significance of this project lies in its potential applications across various domains, including healthcare, sports analysis, and smart home environments. The developed system could find applications in health monitoring, personalized fitness tracking, and creating adaptive environments that respond to human behavior. The project not only contributes to the academic understanding of human activity recognition but also provides practical implications for the development of smart and responsive technologies in our daily lives.