Wearable Technologies and Sports Analytics

U-M Free Access Enrollment Options

What You'll Learn

Understand how wearable devices can be used to help characterize both training and performance.

5 Modules

30 Hours

6 hrs per module (approx.)

Rating

About Wearable Technologies and Sports Analytics

Sports analytics now include massive datasets from athletes and teams that quantify both training and competition efforts. Wearable technology devices are being worn by athletes everyday and provide considerable opportunities for an in-depth look at the stress and recovery of athletes across entire seasons. The capturing of these large datasets has led to new hypotheses and strategies regarding injury prevention as well as detailed feedback for athletes to try and optimize training and recovery.

This course is an introduction to wearable technology devices and their use in training and competition as part of the larger field of sport sciences. It includes an introduction to the physiological principles that are relevant to exercise training and sport performance and how wearable devices can be used to help characterize both training and performance. It includes access to some large sport team datasets and uses programming in python to explore concepts related to training, recovery and performance.

Skills You'll Gain

What You'll Earn

Certificate of Completion:: Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.

Experience Type: 100% Online
Format: Self-Paced
Series: Sports Performance Analytics
Subject: Business

Data Science

Technology
Platform: Coursera

Welcome Message

Welcome to Wearable Technologies and Sports Analytics, an introductory course exploring how wearable devices and large-scale athlete datasets inform training, recovery, and performance. Learners use Python to analyze real sports datasets while developing foundational knowledge in exercise physiology and sport science analytics.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.

Course Schedule

Module 1: Introduction to Wearable Technology

Reading: Wearable Technologies Course Syllabus
Reading: Help Us Learn More About You
Video: Welcome to the Course!
Reading: Introduction to the Gamut Workbook
Video: Introduction to Wearable Technology
Video: Wearable Technology Sensors
Reading: More About Sensors
App Item: Gamut Workbook: How Do You Think Wearables Can Help People?
Video: The Wearables of Athletics
Reading: Jumping Into the G-Vert
Ungraded Lab: Using Python to Explore a Volleyball Dataset
Reading: Week 1 - Assignment Instructions
Ungraded Lab: Week 1 Assignment - Exploring the Volleyball Dataset
Reading: Week 1 - Sample Notebook
Graded: Do You Know Your Wearables?
Graded: Analyzing an Entire Season of Jumping in Volleyball

Module 2: External Loads of Wearable Technology

Video: External Loads of Wearable Technology
App Item: Gamut Workbook: Player Load Relative to Body Mass
Video: Training and Performance Measures
Video: Predicting and Preventing Injury
Graded Assignment: Machine Learning and ACWR
Reading: Machine Learning with Boxing: Identifying Striking Patterns
Discussion Prompt: What Should Be Next for Machine Learning?
Ungraded Lab: Applying ACWR to a Soccer Team Dataset
App Item: Gamut Workbook: Machine Learning Reflection
Reading: Week 2 - Assignment Instructions
Ungraded Lab: Week 2 Assignment Workspace - Applying ACWR to a Soccer Team Dataset (Part 1)
Reading: Week 2 - Sample Notebook
Graded: Do You Know Your External Wearables?
Graded: Applying ACWR to a Soccer Team Dataset (Part 2)

Module 3: Internal Measures of Wearable Technology

Video: Internal Measures of Wearable Technology
Discussion Prompt: The Utility of Internal Measures
Video: Is HR a Passé Measure of Stress? What Can Other Measures Add?
Video: What Is So Magical About Heart Rate Variability?
App Item: Gamut Workbook: Considering the Benefit of Internal Measures for Your Favorite Sport
Video: Evaluating Multiple Internal Measures -- Which Is Best?
Video: Difference Between Chest-Strap and Wrist-Strap HR Data
Ungraded Lab: Evaluating Internal Training Load During Basketball Game (Practice Workbook)
Reading: Week 3 - Assignment Instructions
Ungraded Lab: Week 3 Assignment Workspace - Evaluating Game Intensity (Part 1)
Reading: Week 3 - Sample Notebook
Graded: Internal Measures and the Information They Provide
Graded: Evaluating Game Intensity (Part 2)

Module 4: Combination of Internal and External Wearable Technology

Video: Benefits of Combining Internal and External Meaures
Reading: Estimation of Fitness (Firstbeat Method)
Video: Evaluating External Load Relative to the Internal Load
App Item: Gamut Workbook: Internal and External Measures
Video: Evaluating Internal and External Measures Together to Determine Metrics
Reading: Garmin Metrics
Reading: Stryd
Ungraded Lab: Calculate a “Recovery Variable” Using External Load and HR Data for a Field Hockey Team (Part 1)
Reading: Week 4 - Assignment Instructions
Ungraded Lab: Week 4 Assignment Workspace - Calculating a "Training Intensity Variable"
Reading: Week 4 - Sample Notebook
Graded: Internal and External Metrics
Graded: Calculate a "Training Intensity Variable" Using External Load and HR Data for a Field Hockey Team (Part 2)

Module 5: Global Metrics

Video: Introduction to the Attraction and Dangers of “Global Metrics”
App Item: Gamut Workbook: Global Metrics in Your Own Life
Video: Which Wearable Metrics Do We Not Have a Gold Standard to Compare Against?
Video: Which Wearable Metrics Can We Actually Validate?
Reading: Future of Hydration Prediction
Video: Global Metrics Example: Sleep Score
Video: Testing the Validity of the REM Sleep Measure via Direct Measure With Sleep Study
Reading: (Optional) The Original Validity Testing of REM Sleep
Ungraded Lab: Sleep Metrics Dataset Exploration
Reading: Week 5 - Assignment Instructions
Ungraded Lab: Week 5 Assignment Notebook - Performance Metrics
Reading: Week 5 - Sample Notebook
Reading: Post-Course Survey
Graded: Global Metrics
Graded: Performance Metrics Assessment Quiz

Grading Policy

Grades are cumulative across weekly assignments and quizzes. Learners must achieve 100% on quizzes, with unlimited attempts allowed.

Peter F. Bodary

Clinical Assistant Professor of Applied Exercise Science and Movement Science

Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.

Intermediate Level

Learners should have some familiarity with Python before starting this course. We recommend Python for Everybody Specialization.

Individuals

This experience is available to individual learners on the following platforms:

U-M Community

Students, faculty, staff, and alumni of the University of Michigan get free access.

Organizations

Special pricing and tailored programming bundles available for organizational partners.

4.4

36 Ratings from Coursera

Most Recent Reviews

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August 23, 2025

This is an interesting course with some great real life insights on wearables and messy data from a lecturer who is both clear and very pleasant to listen to. I am giving it 3 stars only because some of the lecture notebooks and assignment pieces, which should be really central to the specialization, suffer from the same type and level of neglect and abandonment as the first 3 courses in the specialization, which is really sad, as it is evident that all the required resources and expertise is given to deliver a neat, tidy, transparent, and coherent course with genuine, meaningful follow-up. But it seems something else was missing. Time, motivation, incentives, we will probably never know, but the sense of "incompleteness" lingers over this course just as much as the previous 3 in the specialization. Also, in this course in particular, sometimes there only seems to be a very vague connection between the lectures and the notebooks and assignments. The lecture notebooks and the assignments sometimes appear as if they had been just tagged to the lecture videos to make them fit in the specialization, as opposed to follow organically from the lecture videos. The addition of scientific papers and other references is great, though. All in all, an enjoyable and interesting offering, but with some very stern caveats.

October 07, 2024

Very interesting. I found the articles and exercises very useful. I would have appreciated some machine learning algorithms

October 11, 2023

This course is relevant and essential for students and professionals looking to understand how technology impacts the field of sports science. t's likely to be valuable for students and professionals in this field. I wasn't interested in sports science, I joined the course, because it also focus on wearable technology. I want to understanding how these devices work, and their performance analysis. I learn the fundamental related to wearable technology and physiological principles.

July 31, 2023

thanks

July 06, 2023

Excellent introductory course on wearables. Makes us think and apply the knowledge gained from the lectures into practical use. Lecturer was good and I was able understand him clearly. This course needs Python and Pandas skills to be at intermediate level. The reading materials provided were also useful. A small problem I faced was with Week 5 lab -- notebooks which explained the concepts in the lab sessions in the previous weeks, was without any explanation for the last week. I'm still not sure why Z-Score metric was used as performance metric. Hopefully they add more context and explanation for future candidates. Despite the small annoyance, I highly recommend the course. Apply for the financial aid you need it and get certified!

June 12, 2023

IN GENERAL TERMS I LIKE IT ALL, WITH THE EXCEPT THAT I COULD NOT FINISH THE SPECIALIZED PROGRAM BECAUSE I DID NOT UNDERSTAND THE QUESTIONS OF COURSE NUMBER 5, THE TEACHER ASKS THINGS THAT HE DOESN'T EXPLAIN, AND WHAT IT EXPLAINES DOES NOT DO IT WITH CLARITY !!! I AM NOT AN EXPERT IN PYTHON, BUT LITTLE BY LITTLE I WAS LEARNING SOMETHING NEW, BUT COURSE NUMBER 5 SEEMED IMPOSSIBLE. I AM AN EXPERT IN ANALYZING SPORTS STATISTICS, AND I TAKEN THE SPECIALIZED PROGRAM BECAUSE I WANTED TO LEARN NEW THINGS THAT WILL HELP ME IN MY JOB; AND IN COURSE 4 I LEARNED MANY NEW AND VERY INTERESTING THINGS; BUT I COULDN'T FINISH THE SPECIALIZATION BECAUSE COURSE NUMBER 5 IS ANTI-PEDAGOGICAL IF YOU ARE NOT AN EXPERT IN PYTHON I DO NOT RECOMMEND THIS COURSE !!!

Wearable Technologies and Sports Analytics

What You'll Learn