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


  • 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.

date/time interval

  • 2019 - 2020