Applying Adaptive Intelligent Tutoring Techniques to Physical Fitness Training Programs

Voge, J.1, Negri, A.1, Woodall, P.1, Thayer, D.1, Ruby, B.2, Hailes, W.2, Reinert, A.3, Niehaus, J.1, and Lynn, S.1

To appear in Proceedings of the 3rd International Conference on Adaptive Instructional Systems, held as part of the 23rd International Conference on Human-Computer Interaction, Washington, DC (July 2021)

Adaptive Training Protocols (ATP) is a collection of algorithms and software to apply principals of intelligent tutoring to physical fitness training. To obtain norming data for ATP, we examined exercise performance from 34 participants under an adaptive workout regimen lasting 13 weeks. The goal of the regimen was to train to pass the performance criteria of the US Marine Corps Initial Strength Test (IST; a 1.5-mile run, sits-ups, pull-ups, and push-ups). The weekly regimen comprised an IST, an interval workout, and a maximum workout. Adaptation was accomplished via two algorithms: maximum-day reps were double those accomplished on the prior IST and maximum-day and interval-day runs were performed at specified rates of perceived exertion. Starting capabilities for run, sit-ups, and push-ups negatively correlated with progression rates; participants who exhibited lower performance at the start of the study made steeper gains in performance. Individual logistic curve fitting found decelerating, inflecting, and accelerating progression profiles. Participants showed considerable variation in their profiles both across individuals in each exercise and within individuals across exercises. Progression profiles can be used to forecast the performance that a person can attain in a given timeframe under a given training regimen. This knowledge can be used to adapt the workout to provide more time to reach a goal if needed or to focus on exercises that are in jeopardy of not achieving the goal in time. ATP will help the Marine Corps plan for when intended recruits may be physically ready to ship out to boot camp.

1 Charles River Analytics
2 Montana Center for Work Physiology and Exercise Metabolism, University of Montana
3 University of Montana

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