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

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 .

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.

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 .