I-Corps: Adaptable Speed-Endurance Training Algorithm to Manage Aerobic and Anaerobic Capacity Development

I-Corps：用于管理有氧和无氧能力发展的适应性速度耐力训练算法

• 批准号: 1924814
• 负责人: steven riechman
• 金额: $ 5万
• 依托单位: Texas A&M University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1924814&HistoricalAwards=false
• 关键词: Corps; Adaptable; Speed; Endurance; Training

The broader impact/commercial potential of this I-Corps project is to integrate novel and established wearable physiological sensors, machine learning and an adaptable exercise-training algorithm to optimize performance and minimize risk of injury or overtraining. Professional and collegiate teams face significant problems due to injuries, overexertion (rhabdomyolysis) and exertional heat illness (EHI). Nearly half of these issues are avoidable via proper management and training. In elite athletes, achieving the balance of over or undertraining remains difficult and elusive. Elite and professional athletes, and their coaches, sports scientists, and elite team trainers require the most precise assessment and management of training and performance and the cost of failure is much greater. With success in this population, this innovation can potentially be adaptable to fitness and wellness applications where precision is less important, and efficiency and motivation is more highly valued. For chronic disease management applications, precision, physiological monitoring, instant feedback, and exercise progression become vitally important.This I-Corps project seeks to enable real-time and continuous monitoring of physiological responses to exercise training using a custom sensor array, adaptable speed-endurance training algorithm and integrated machine learning to create an optimal, individualized exercise-training program for peak performance and minimize injury in elite and professional athletes. Currently, the primary way in which athletes manage this lacks integration and individualization of physiological data to workload in order to prevent overtraining during the exercise session. To date, there have been few standardized and progressive non-steady state training programs that can adjust a program to a specific individual. Using real-time novel and established physiological sensors with machine learning, the training algorithm has the capability to adapt a workout to an individual's specific physiological responses. A novel sensor developed for this project has proven effective in preliminary testing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个I-Corps项目更广泛的影响/商业潜力是整合新颖和成熟的可穿戴生理传感器，机器学习和适应性强的运动训练算法，以优化性能并最大限度地降低受伤或过度训练的风险。专业和大学团队面临着严重的问题，由于受伤，过度劳累（横纹肌溶解症）和劳累性中暑（EHI）。 这些问题中有近一半可以通过适当的管理和培训来避免。在精英运动员中，实现过度训练或训练不足的平衡仍然是困难和难以捉摸的。精英和专业运动员及其教练、体育科学家和精英团队训练师需要对训练和表现进行最精确的评估和管理，失败的代价要大得多。随着这一人群的成功，这项创新可能适用于健身和健康应用，其中精度不太重要，效率和动力更受重视。对于慢性疾病管理应用，精确性、生理监测、即时反馈和运动进展变得至关重要。I-Corps项目旨在通过使用定制传感器阵列、自适应速度耐力训练算法和集成机器学习，实时和连续监测运动训练的生理反应，以创建最佳的，个性化的运动训练计划，使精英和专业运动员达到最佳表现，并最大限度地减少伤害。目前，运动员管理这一点的主要方式缺乏生理数据与工作负荷的整合和个性化，以防止在锻炼期间过度训练。 迄今为止，几乎没有标准化和渐进的非稳态训练计划，可以调整程序到特定的个人。通过使用具有机器学习功能的实时新型和成熟的生理传感器，训练算法能够使锻炼适应个人的特定生理反应。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。