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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/wearable-technologies
课程评论：没有评论
课程名称：可穿戴技术与体育分析 课程概述：体育分析如今包含来自运动员和团队的大规模数据集，这些数据量化了训练和比赛的努力。运动员日常佩戴的可穿戴技术设备为我们深入了解运动员在整个赛季中的压力和恢复提供了重要机会。这些大数据集的捕获带来了关于伤害预防的新假设和策略，以及为运动员提供详细反馈的可能，以帮助优化训练和恢复。本课程介绍了可穿戴技术设备及其在训练和比赛中的应用，属于更广泛的运动科学领域。课程包括与运动训练和表现相关的生理学原理，以及如何利用可穿戴设备来帮助描述训练和表现。学员将获取一些大运动团队的数据集，并使用Python编程来探索与训练、恢复和表现相关的概念。 课程大纲： 1. **可穿戴技术简介**：介绍运动员和团队使用的不同类型可穿戴设备及其在训练和恢复中的作用，强调传感器类型以及数据如何提供训练强度和生理“准备度”等见解。 2. **可穿戴技术的外部负荷**：聚焦于“外部”测量，包括对“负荷”和“努力”外部测量的一些不准确假设，探讨持续使用可穿戴设备对努力量化的机会，以及对减少伤害和提升表现的潜在影响，介绍“急性与慢性负荷”的概念。 3. **可穿戴技术的内部测量**：深入探讨训练和恢复的生理学，介绍“内部”测量的使用，关注运动员对训练和比赛带来的压力反应的评估，讨论内部测量在个人和团队训练及恢复评价中的优缺点。 4. **内部和外部可穿戴技术的结合**：结合内部和外部测量，提供对训练和恢复更细致的分析，外部测量量化运动，而内部测量提供运动员对训练的耐受反馈，有助于评估表现改进和防止过度使用伤害。 5. **全球指标**：讨论许多现代消费设备开发和使用的新兴全球指标，强调这些新指标的兴奋之处及其局限性，探讨实际使用的传感器及其可验证的领域。 这个课程旨在为学员提供可穿戴技术在体育科学应用中的基础知识和实践技能，以推动运动表现和安全。
Name:Introduction to Wearable Technology
Description: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”.
Name:External Loads of Wearable Technology
Description: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.
Name:Internal Measures of Wearable Technology
Description: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.
Name:Combination of Internal and External Wearable Technology
Description: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.
Name:Global Metrics
Description: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.
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.
