Wearable Technologies and Sports Analytics (Coursera)

Wearable Technologies and Sports Analytics (Coursera)
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Learners should have some familiarity with Python before starting this course. We recommend Python for Everybody Specialization.
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Wearable Technologies and Sports Analytics (Coursera)
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.

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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.


Course 4 of 5 in the Sports Performance Analytics Specialization


Syllabus


WEEK 1

Introduction to Wearable Technology

In this module, we will introduce different types of wearable devices that are used by athletes and teams to improve training and recovery. We will start by highlighting what types of sensors are used within the wearable devices and how the data coming from these sensors can provide insights, such as training intensity and or physiologic “readiness”.


WEEK 2

External Loads of Wearable Technology

In this module, we will focus on what we have introduced as “external” measures. We will point out some of the (inaccurate) assumptions that are made regarding external measures of “load” and “effort”. In addition, we will outline how the continuous use of wearable devices has led to new opportunities for quantifying effort as well as (in theory) reducing injury and improving performance. We will finish by describing the “acute to chronic workload” and the reasons it has gained a lot of attention in the past several years.


WEEK 3

Internal Measures of Wearable Technology

In this module, we will dive more into the physiology of training and recovery, focusing on what we have introduced as “internal” measures. We will further explore the use of internal sensors to provide a glimpse of how the individual athlete is responding to the stress induced by training and/or competition. We will also highlight the pros and cons of using internal measures to evaluate individual and team training and recovery.


WEEK 4

Combination of Internal and External Wearable Technology

In this module, we combine external and internal measures to provide a much more nuanced look at training and recovery. The external measures can provide a highly quantified evaluation of the movements and motions that have taken place, while the internal measures provide feedback about how the athlete is tolerating the training. Combining them can be instrumental for evaluating performance improvements and preventing or reducing overuse injuries.


WEEK 5

Global Metrics

In this module, we will discuss the exciting new global metrics that have been developed and/or used by many of the consumer devices that are available today. Although these new metrics are exciting, we want to be cognizant of the limitations of these devices. Therefore, we will discuss what sensors are actually employed to provide these new metrics and highlight where validation is feasible.



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MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Course Auditing
32.00 EUR/month
Learners should have some familiarity with Python before starting this course. We recommend Python for Everybody Specialization.

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.