IoT Sensor-Based Activity Recognition

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Details

This book offer clear descriptions of the basic structure for the recognition and classification of human activities using different types of sensor module and smart devices in e.g. healthcare, education, monitoring the elderly, daily human behavior, and fitness monitoring. In addition, the complexities, challenges, and design issues involved in data collection, processing, and other fundamental stages along with datasets, methods, etc., are discussed in detail. The book offers a valuable resource for readers in the fields of pattern recognition, humancomputer interaction, and the Internet of Things.



Highlights recent research in the field of Human Activity Recognition (HAR) based on sensors Presents a comprehensive study and addresses various aspects of human activity recognition based on wearable, environmental, and smartphone sensors Includes a list of sensing devices, sensors, and applications that can be used for collecting activity data Provides a comprehensive analysis of ca. 80 benchmark datasets, which includes information on sensors, attributes, activity classes, etc., and sums up several types of sensor-based daily activities, medical activities, fitness activities, device usage, fall detection, and hand gesture data

Klappentext

Introduction on Sensor-based Human Activity Analysis: Background.- Basic Structure for Human Activity Recognition Systems: Preprocessing and Segmentation.


Inhalt
Introduction on Sensor-based Human Activity Analysis: Background.- Basic Structure for Human Activity Recognition Systems: Preprocessing and Segmentation.

Weitere Informationen

  • Allgemeine Informationen
    • GTIN 09783030513788
    • Sprache Englisch
    • Genre Allgemeines & Lexika
    • Lesemotiv Verstehen
    • Größe H241mm x B160mm x T18mm
    • Jahr 2020
    • EAN 9783030513788
    • Format Fester Einband
    • ISBN 3030513785
    • Veröffentlichung 31.07.2020
    • Titel IoT Sensor-Based Activity Recognition
    • Autor Md Atiqur Rahman Ahad , Anindya Das Antar , Masud Ahmed
    • Untertitel Human Activity Recognition
    • Gewicht 506g
    • Herausgeber Springer
    • Anzahl Seiten 224

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