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2024 | OriginalPaper | Chapter

Human Activity Recognition: Approaches, Datasets, Applications, and Challenges

Authors : Alisha Banga, Ravinder Ahuja, S. C. Sharma

Published in: Modern Approaches in IoT and Machine Learning for Cyber Security

Publisher: Springer International Publishing

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Abstract

The chapter delves into Human Activity Recognition (HAR), a crucial aspect of the Internet of Things (IoT), focusing on classifying human movements using sensors and machine learning. It begins by introducing the importance of HAR and its various applications, such as healthcare monitoring, security surveillance, and smart home technologies. The chapter then explores different approaches to HAR, including sensor-based, vision-based, and multi-modal techniques, highlighting the advancements in machine learning and deep learning algorithms. It also discusses several publicly available datasets used for training these algorithms, such as UCI-HAR and WISDM. Additionally, the chapter addresses the challenges faced in HAR, including sensor deployment, feature extraction, and privacy concerns. By offering a detailed analysis of the current state and future prospects of HAR, this chapter serves as an essential resource for professionals seeking to understand and advance this rapidly evolving field.
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Metadata
Title
Human Activity Recognition: Approaches, Datasets, Applications, and Challenges
Authors
Alisha Banga
Ravinder Ahuja
S. C. Sharma
Copyright Year
2024
Book
Modern Approaches in IoT and Machine Learning for Cyber Security

Print ISBN: 978-3-031-09954-0

Electronic ISBN: 978-3-031-09955-7

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-3-031-09955-7_7

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