Applied Machine Learning for Health and Fitness: A Practical Guide to Machine Learning with Deep Vision, Sensors and Iot

ISBN-13: 9781484257715 / Angielski / Miękka / 2020 / 259 str.

Kevin Ashley

Spis treści

Introduction

Machine Learning is fun with sensors and sports. Today’s data scientist is out there, on the ski slopes, or surfing the waves, and best way to apply machine learning is real life scenarios of sports. What can we do if we had the best, the ultimate model of our body and health monitoring us constantly? So, when we wanted to start a new sport, for example skiing or surfing, our personal body assistant could give us suggestions, like a personal coach. With machine learning and AI methods, imagine having a coach next to you 24/7.

Part I: Sensors

Chapter 1: Getting Started

Why are sensors important for health and fitness? For coaches, athletes and health professionals, they provide and objective picture of your activity. It’s often impossible to capture micro-movements and forces of a downhill racer, moving at 100 mph down a winding ski trail, but when equipped with sensors, every aspect of that movement can be captured, analyzed and studied. In this book we’ll use various IoT devices that can be used for sports data collection: inertial measurement units (IMUs), attitude and heading reference systems (AHRS), inertial navigation systems (INS/GPS), pressure sensors and others.

1. Types of sensors and what they measure

a. IMUs, AHRS

b. INS/GPS

c. Pressure sensors

d. Heart rate

e. Vision and camera

2. Sport science and data

a. Why is data frequency so important? A typical GPS device in your mobile phone works at 1Hz, that is one reading per second. Why isn’t this enough for most sports applications?

b. Machine Learning really cares about data frequencies, as a rule of thumb we will use 100 Hz for most sensor data we collect

3. How can Machine Learning help?

a. Problems solved by machine learning for human movement, health and fitness applications

4. Visualizing sports from sensor data

Project: First look at athlete movement analysis with a sample sensor data set

Chapter 2: Sensor Hardware

It turns out they don’t sell sensors with built in machine learning at convenience stores just yet! So, we made some. We go over some sport specific requirements for sensors, where and how sensors are placed on the body and equipment. In this chapter we will cover choices for sensor hardware, communication from sensors for data collection and data choices for IoT devices. 

1) Sensor IoT devices: IMU, AHRS, INS/GPS, Pressure, Proximity

2) Sensor communication

3) Data choices for IoT devices

Project: Learning to work with a sample SensorKit dataset

Chapter 3: Sensor Software

Our sensor is operating at a relatively high frequency of 100 samples per second (100 Hz). We need a special software to connect our sensor to the app. In this chapter we include a practical project on how to connect our sensor via a protocol like Bluetooth Low Energy to a mobile device and transfer data to the cloud.

1) Sensor firmware

2) Algorithms for sensor data processing

3) Connecting with the app and the SDK

Project: Writing the code to connect from sensor to the cloud

Chapter 4: 3D Printing Sensors

Project: 3D printing is a fantastic technology for custom applications like sports! In this chapter I included a fun project on designing the case for our sensor, using 3D design software like Fusion 360 and 3D printing our sensor.

1) Designing sensor casing model for sports

2) Printing the sensor

3) Every sport is different!

Project: Designing a case and 3D printing our sensor

Part II: Sensor Data

Sensors generate an enormous amount of data! In this part we learn about different types of sensor data, how to parse it, store it, transfer between IoT devices and the cloud.

Chapter 5: Collecting sensor data

This is where we sports scientists have most fun: data science on the ski slopes and surfing the waves! In this chapter I included a project.

1) Sports and sensor placement

2) Designing sports experiments

3) Software and mobile devices for sports

4) Sensor data for ML

Project: Collecting dribble data from a basketball sensor

Chapter 6: Storing and parsing data

Storing sensor data is an interesting subject: at 100 Hz we have a lot of data from sports!

1) Data frequency and aggregation decisions

2) What to calculate on the sensors

3) Sending data to the cloud

Project: Writing code to parse and store sensor data

Chapter 7: Managing and streaming IoT data in the cloud

An overview of modern IoT data technologies for the cloud, this chapter is about managing and streaming IoT data in the cloud.

1) Non-relational databases for sensor data 

2) Streaming IoT data: (Spark, Kafka, Azure Stream Analytics)

3) Data pipelines for IoT

Project: Storing and streaming IoT data in the cloud

Part III: Machine Learning for Health, Fitness and Sports

From sensor data to physics of sports, movement analysis and machine learning models.

Chapter 8: Physics of sports

Sports scientists believe that each sport can be described mathematically with physics, let’s dive into sport science! In this chapter we’ll have a physics project to help us better understand the models.

1) Physics of movement

2) Sensors and physics

Project: Calculating forces for an athlete, using physics

Chapter 9: Machine Learning models

Machine Learning models for sports. This chapter defines reasoning behind various algorithms for machine learning in sports, as applied to sensor data.

1) Raw sensor data

2) Clean and transform the data

3) Engineering features

4) Supervised Learning

5) Unsupervised Learning

6) Reinforcement Learning

Project: Creating a machine learning model from our experiments

Chapter 10: Applying Machine Learning for various activities

In this chapter we look at some applications of sensors for sports, fitness and health. 

1) Skiing and snowboarding

2) Basketball

3) Tennis

4) Diving

5) Javelin

6) Surfing

Part III: Visualizing Sensors 

Using computer vision and visualizing sports data in 3D and VR.

Chapter 11: Computer vision

Computer vision is an important way of tracking athletes in real time.

1) Computer vision for sports overview

2) 3D body rendering

3) Problems with computer vision vs sensors (occlusion)

4) Winning scenario: combining sensors with computer vision

5) Project: using computer vision for athletic performance

Project: using computer vision for athletic performance

Chapter 12: Visualizing athlete in 3D, Holograms and VR

In this chapter we’ll touch the holy grail of sports science: visualizing athlete in full 3D, as a holographic avatar.

1) Methods and requirements for 3D visualization

2) Using Unity to visualize data

 

Chapter 13: Vision and Sensors

This chapter is about combining vision and sensors. Imagine, if we had to bring visual and sensor data together, then we have a tool that can provide both a near-real time visual feedback and video analysis.

1) Combining sensor and video data

Project: Combining sensor and video data for analysis

Part V: What the Coach needs

From individual athletes to the team: this chapter would make the coach happy! Often, tracking an individual athlete with sensors is not enough: coaches or health professionals deal with teams they need to analyze. 

Chapter 14: Coach and team view on the data

Working with coaches on US Olympic Team, WTA, WNBA, professional ski and snowboard instructors, I learned a lot about requirements that coaches have on the sensors, data, analytics and presentation of the data.

1) Coaches and teams view

2) Looking across the entire team

3) Coach dashboard (PowerBI)

Project: Creating a coach dashboard with PowerBI

Chapter 15: Connected sensors and sports teams

From individual athletes and sports, to connected experiences.

1) Sensor data from the team prospective

2) Connected team

Conclusion: What’s next

This book provides a toolkit, a foundation for a sports scientist or a data professional to use sensors and machine learning for insights about athlete performance and injury prevention.

PROJECTS

1) First look at athlete movement analysis with a sample sensor data set

2) Learning to work with a sample sport dataset  

3) Writing the code to connect from sensor to the cloud

4) Writing code to parse and store sensor data

5) Storing and streaming IoT data in the cloud

6) Designing a case and 3D printing our sensor

7) Collecting dribble data from a basketball sensor

8) Calculating forces for an athlete, using physics

9) Creating a machine learning model from our experiments 

10) Using computer vision for athletic performance

11) Combining sensor and video data for analysis

12) Creating a coach dashboard with PowerBI for the team

O autorze

Kevin Ashley is a Microsoft architect, IoT expert, and professional ski instructor. He is an author and developer of top sports and fitness apps and platforms such as Active Fitness and Winter Sports with a multi-million user audience. Kevin often works with sports scientists, Olympic athletes, coaches and teams to advance technology use in sports. 

Blurb

Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data scientist, machine learning practitioner or anyone interested in AI applications. These ML models and methods can be used to create solutions for AI enhanced coaching, judging, athletic performance improvement, movement analysis, simulations, in motion capture, gaming, cinema production and more.

Packed with fun, practical applications for sports, machine learning models used in the book include supervised, unsupervised and cutting-edge reinforcement learning methods and models with popular tools like PyTorch, Tensorflow, Keras, OpenAI Gym and OpenCV. Author Kevin Ashley—who happens to be both a machine learning expert and a professional ski instructor—has written an insightful book that takes you on a journey of modern sport science and AI. 

Filled with thorough, engaging illustrations and dozens of real-life examples, this book is your next step to understanding the implementation of AI within the sports world and beyond. Whether you are a data scientist, a coach, an athlete, or simply a personal fitness enthusiast excited about connecting your findings with AI methods, the author’s practical expertise in both tech and sports is an undeniable asset for your learning process. Today’s data scientists are the future of athletics, and Applied Machine Learning for Health and Fitness hands you the knowledge you need to stay relevant in this rapidly growing space.

You will:

Use multiple data science tools and frameworks

Apply deep computer vision and other machine learning methods for classification, semantic segmentation, and action recognition

Build and train neural networks, reinforcement learning models and more

Analyze multiple sporting activities with deep learning

Use datasets available today for model training

Use machine learning in the cloud to train and deploy models

Apply best practices in machine learning and data science