Pocket AI and IoT: Turn Your Phone Into a Smart Fitness Tracker
Pocket AI and IoT: Turn Your Phone Into a Smart Fitness Tracker

Abstract: 

Want to learn more about trends like AI, IoT and wearable tech? In this workshop, we will cut through the hype by building a “smart” fitness tracker using your own mobile device. We’ll do hands-on exercises: you’ll acquire data from sensors, design a step counter and train a human activity classifier. You will leave motivated and ready to use machine learning and sensors in your own projects!  
The goal for this session is twofold: show our participants how easy it is to get started with Machine Learning and wearables and set them up for success to take on challenging projects in this domain. A clear instruction sheet will guide the participants through exercises ranging from beginner to intermediate level. This workshop is highly interactive with hands-on exercises in which the participants will create a “smart” wearable device and test it out. We aim to demonstrate how powerful applications can be built when we combine machine learning, sensor analytics, and IoT. Participants will use the free MATLAB Mobile App to access built-in sensors and analyze data on their mobile devices.

● MATLAB Online;
● MATLAB Mobile;
● MATLAB Drive;
● mobile devices such as a phone or tablet;

https://tinyurl.com/PocketAIODSCEastPreWork
https://drive.google.com/open?id=1mYuGLQ3FO6gKxRS-oVkCzIDPz0yswUxK

Bio: 

Vaidehi is a principal engineer at MathWorks with over 10 years of software engineering experience. She leads the efforts to generate embedded efficient deep learning network representations using quantization and model compression techniques. In the past, she has developed software tools for automatic deployment of embedded applications on microprocessors, ECUs and FPGAs. Vaidehi has a bachelor’s degree in Information Systems and master’s degree in Computer Science with a specialization in Machine Learning. Prior to MathWorks, Vaidehi was a software engineer at Bloomberg working on solutions that provide e-communication surveillance. Vaidehi has presented technical talks at the MathWorks Research Summit and the MathWorks Advisory Board meetings.