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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Wearable Technologies and Sports Analytics
Ce cours fait partie de Spécialisation Sports Performance Analytics
Instructeur : Peter F. Bodary
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Ce que vous apprendrez
Understand how wearable devices can be used to help characterize both training and performance.
Compétences que vous acquerrez
- Catégorie : Data Analysis
- Catégorie : Python Programming
- Catégorie : sports analytics
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Il y a 5 modules dans ce cours
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”.
Inclus
4 vidéos7 lectures2 devoirs1 élément d'application2 laboratoires non notés
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.
Inclus
3 vidéos3 lectures3 devoirs2 éléments d'application1 sujet de discussion2 laboratoires non notés
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.
Inclus
5 vidéos2 lectures2 devoirs1 élément d'application1 sujet de discussion2 laboratoires non notés
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.
Inclus
3 vidéos5 lectures2 devoirs1 élément d'application2 laboratoires non notés
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.
Inclus
5 vidéos5 lectures2 devoirs1 élément d'application2 laboratoires non notés
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University of Michigan
University of Michigan
University of Michigan
The State University of New York
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