Smartphone-Based Human Activity Recognition (Hardcover, 2015 ed.)

The book reports on the author's original work to address the use of today's state-of-the-art smartphones for human physical activity recognition. By exploiting the sensing, computing and communication capabilities currently available in these devices, the author developed a novel smartphone-based activity-recognition system, which takes into consideration all aspects of online human activity recognition, from experimental data collection, to machine learning algorithms and hardware implementation. The book also discusses and describes solutions to some of the challenges that arose during the development of this approach, such as real-time operation, high accuracy, low battery consumption and unobtrusiveness. It clearly shows that it is possible to perform real-time recognition of activities with high accuracy using current smartphone technologies. As well as a detailed description of the methods, this book also provides readers with a comprehensive review of the fundamental concepts in human activity recognition. It also gives an accurate analysis of the most influential works in the field and discusses them in detail. This thesis was supervised by both the Universitat Politecnica de Catalunya (primary institution) and University of Genoa (secondary institution) as part of the Erasmus Mundus Joint Doctorate in Interactive and Cognitive Environments.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Springer Theses
Release date:	2015
First published:	2015
Authors:	Jorge Luis Reyes Ortiz
Dimensions:	235 x 155 x 14mm (L x W x T)
Format:	Hardcover - Cloth over boards
Pages:	133
Edition:	2015 ed.
ISBN-13:	978-3-319-14273-9
Categories:	Books > Computing & IT > Social & legal aspects of computing > Human-computer interaction Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > Robotics Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics > General
LSN:	3-319-14273-9
Barcode:	9783319142739

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