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Книга Smartphone Sensor-based Human Activity Recognition System Nurul Amin Choudhury

Smartphone Sensor-based Human Activity Recognition System

In-Depth Design Analysis with New Tools and Techniques

Автор: Nurul Amin Choudhury, Badal Soni
Език: Английски език
Корици: С твърди корици
Издател: Springer, Berlin
Наличност: Налично при издателя, по поръчка
Изпращаме след 17-27 дни
174.01 € 340.34 лв
This book provides an in-depth analysis of the design and implementation of a smartphone sensor-base...

Информация за книгата

Автор
Nurul Amin Choudhury, Badal Soni
Език
Английски език
Корици
Книга - С твърди корици
Издадена
2026
страници
153
EAN
9789819572052
Enbook ID
50310473
Издател
Springer, Berlin
Теглоt
297
Размери
155 x 235

Пълно описание

This book provides an in-depth analysis of the design and implementation of a smartphone sensor-based Human Activity Recognition (HAR) system using advanced learning models. It offers detailed insights into key advancements and future directions for HAR system development. The content includes a comprehensive literature review on wearable sensor-based HAR, along with descriptions of publicly available datasets for building HAR systems. To overcome the limitations of existing sensor-based HAR systems, the book emphasizes the creation of a robust HAR dataset in uncontrolled environments. This dataset is developed using built-in smartphone sensors, enabling real-time activity classification. The book presents multiple effective and optimized classifiers for developing efficient HAR systems, leveraging both self-generated and publicly available data.

The book demonstrates high effectiveness in real-time activity classification and addresses common challenges such as class imbalance and domain shift. It provides practical methodologies and techniques to enhance HAR system performance. This book is an essential resource for professionals, healthcare practitioners, academics, researchers, and students specializing in health applications, signal processing, machine learning, ensemble learning, and deep learning. It offers extensive analysis and actionable insights for investigators, helping streamline product development through intelligent learning algorithms in the field of Human Activity Recognition.

 

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