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Nagender Kumar Suryadevara - Quick-start Guide to Gait Analysis with JavaScript and TensorFlow.js

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Nagender Kumar Suryadevara Quick-start Guide to Gait Analysis with JavaScript and TensorFlow.js
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Book cover of Beginning Machine Learning in the Browser Nagender Kumar - photo 1
Book cover of Beginning Machine Learning in the Browser
Nagender Kumar Suryadevara
Beginning Machine Learning in the Browser
Quick-start Guide to Gait Analysis with JavaScript and TensorFlow.js
1st ed.
Logo of the publisher Nagender Kumar Suryadevara School of Computer and - photo 2
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Nagender Kumar Suryadevara
School of Computer and Information Sciences, University of Hyderabad, Hyderabad, Telangana, India

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-6842-1 . For more detailed information, please visit http://www.apress.com/source-code .

ISBN 978-1-4842-6842-1 e-ISBN 978-1-4842-6843-8
https://doi.org/10.1007/978-1-4842-6843-8
Apress standard
Nagender Kumar Suryadevara 2021
This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Distributed to the book trade worldwide by Springer Science+Business Media New York,1 NY Plazar, New York, NY 10014. Phone 1-800-SPRINGER, fax (201) 348-4505, e-mail orders-ny@springer-sbm.com, or visit www.springeronline.com. Apress Media, LLC is a California LLC and the sole member (owner) is Springer Science + Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation.
Preface

In recent times, artificial intelligence (AI) and machine learning (ML) techniques have been widely used in many applications , such as monitoring environmental parameters, monitoring, and control of industrial situations, intelligent transportation, structural health monitoring, health care, and so on. The advancement of electronics, embedded control, smart sensing, networking, and communication has made it possible to develop low-cost smart systems. Although there are smart systems, the computing capabilities are minimal, and hence they are considered to be resource-constrained computing devices (e.g., mobile phones, smart watches, and mini electronic gadgets).

Applying smart strategies involving complex mathematical operations of AI and ML methods on resource-constrained computing devices and browsers is challenging. The advancements in Internet technologies, primarily JavaScript skills, have made it possible to execute AI/ML models in the browsers and resource computing devices. There are many other publications on AI, ML, and JavaScript, but this book provides beneficial information and practical knowledge to develop intelligent methods/models from scratch and deploy them on browsers and resource-constrained computing devices.

The complete book is divided into six chapters:

Chapter describes the fundamentals of web development. This chapter provides a short description of designing and developing web applications using web building blocks. For developing an AI/ML model and running on the browser or resource-constrained computing device, this chapters practical knowledge is essential. For a beginner in any field of study and interested in developing web apps, this chapter provides the necessary practical skills to better realize web applications.

Chapter delivers the steps to be performed and the necessary JavaScript libraries to be considered for processing the data at the computers browser application level. The latest JavaScript libraries (p5.js and ml5.js) that help build the AI/ML models with practical steps are covered.

Chapter introduces the human pose estimation application as an example that enables the reader to understand how an AI/ML model involving complex mathematical operations can be used to run in the browser. The stepwise procedure teaches you how to implement ML methods to estimate an individuals poses.

Chapter covers the open-source JavaScript library TensorFlow.js, which will be useful for building and deploying AI/ML models in the browser. The architecture of the TensorFlow.js, including its accelerators that support massive data processing at the browser, is explained. Practical examples of executing a neural network model for useful classification tasks are elucidated using the TensorFlow.js library.

Chapter examines how to determine gait parameters by applying AI/ML methods along with the JavaScript libraries in the web browser application. The chapter walks you through the basics of gait analysis and expands on the observational method considered in determining the vital parameters for analysis using the AI in the browser.

Chapter provides a few more advanced applications to run on the browser by applying the AI/ML methods. This chapter encourages the reader to think about the advancements possible when running AI/ML models in the browser.

I hope that you enjoy reading the book. If you need any help whatsoever with the practicals, please feel free to contact me.

Dr. S. Nagender Kumar Suryadevara

Associate Professor, School of Computer and Information Sciences,

University of Hyderabad, India.

Acknowledgments

This journey would not have been possible without the support of my family, professors, mentors, and friends. I am especially grateful to my parents, who supported me emotionally. To my family, thank you for encouraging me in all of my pursuits and inspiring me to follow my dreams.

I want to express my sincere gratitude and heartfelt thanks to Vishwesh Ravi Shrimali for allocating some time and taking responsibility for reviewing the book chapters and providing valuable comments and helpful suggestions.

Thanks to everyone in the publication team, especially Aaron Black, who helped me in getting the book content into great shape, James Markham, who took great pains in grooming the book, and Jessica Vakili, who made sure the book writing process went smoothly and on time.

I am indebted to many of my students and colleagues who were involved with various projects over several years, and some of their works have been used in this book. I would especially like to give credit to one of my students, Ashish Gupta, for helping me in executing the programs in Chapter .

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