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Human Activity Recognition
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
The Human Activity Recognition (HAR) system is a prominent research area that aims to develop intelligent algorithms and systems capable of automatically identifying and classifying human activities based on sensor data. This project report provides a thorough exploration of the key aspects involved in the design, implementation, and evaluation of a Human Activity Recognition system. The report begins with a comprehensive review of the existing literature, covering the fundamental concepts, methodologies, and recent advancements in HAR. It discusses various sensor modalities commonly used for activity recognition, including accelerometer, gyroscope, and magnetometer data. Special attention is given to machine learning and deep learning techniques employed in the recognition process. The project involves the development of a prototype HAR system using state-of-the-art techniques. The implementation utilizes a dataset representative of diverse human activities to train and test the system. The chosen methodology is presented in detail, highlighting the selection and preprocessing of sensor data, feature extraction, and the training of machine learning or deep learning models.
How to cite this paper
@article{1705380,
author = {Pramodh Yadav M, Purvika S, Chatur S},
title = {Human Activity Recognition},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {7},
pages = {277-283},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705380.pdf},
abstract = {The Human Activity Recognition (HAR) system is a prominent research area that aims to develop intelligent algorithms and systems capable of automatically identifying and classifying human activities based on sensor data. This project report provides a thorough exploration of the key aspects involved in the design, implementation, and evaluation of a Human Activity Recognition system.
The report begins with a comprehensive review of the existing literature, covering the fundamental concepts, methodologies, and recent advancements in HAR. It discusses various sensor modalities commonly used for activity recognition, including accelerometer, gyroscope, and magnetometer data. Special attention is given to machine learning and deep learning techniques employed in the recognition process. The project involves the development of a prototype HAR system using state-of-the-art techniques. The implementation utilizes a dataset representative of diverse human activities to train and test the system. The chosen methodology is presented in detail, highlighting the selection and preprocessing of sensor data, feature extraction, and the training of machine learning or deep learning models.},
month = {January},
}