Dynamic Tension Optimization Model
DTOM categorizes muscle fibers into Type I and Type II, recognizing their distinct responses to exercise. Type I fibers, which are more endurance-oriented, can handle longer periods of tension and recover swiftly. For these fibers, DTOM proposes engaging them with frequent, yet shorter bursts of tension, paired with relatively brief rest intervals. This strategy is aimed at enhancing endurance without overtaxing the muscle.
For the Type II fibers, which are more suited to power and fatigue more quickly, the model suggests intense, brief periods of tension followed by longer rest periods. This design is intended to maximize power output while minimizing the risk of fatigue, aligning with the fibers' fast-twitch nature and higher fatigue rate.
DTOM also takes into account how different energy systems are activated during workouts with varying durations of TUT. Longer TUT periods predominantly tax the glycolytic and oxidative systems, necessitating extended recovery to fully replenish energy stores—ideal for sessions targeting muscle hypertrophy or endurance. Shorter durations, however, rely more heavily on the ATP-CP system, suggesting longer rest intervals to allow complete ATP replenishment, crucial for peak performance in strength-focused exercises.
By leveraging extensive datasets from previous real-time monitoring phases, DTOM in Autonomy v2 can predictively adjust TUT and rest periods based on historical data. This predictive capability allows the model to effectively customize workout plans tailored to the individual’s historical responses, maximizing the efficacy of each session without the need for live data tracking. This method ensures that training remains both personalized and efficient, optimizing muscle engagement and recovery based on past insights to drive better overall fitness results.
For the Type II fibers, which are more suited to power and fatigue more quickly, the model suggests intense, brief periods of tension followed by longer rest periods. This design is intended to maximize power output while minimizing the risk of fatigue, aligning with the fibers' fast-twitch nature and higher fatigue rate.
DTOM also takes into account how different energy systems are activated during workouts with varying durations of TUT. Longer TUT periods predominantly tax the glycolytic and oxidative systems, necessitating extended recovery to fully replenish energy stores—ideal for sessions targeting muscle hypertrophy or endurance. Shorter durations, however, rely more heavily on the ATP-CP system, suggesting longer rest intervals to allow complete ATP replenishment, crucial for peak performance in strength-focused exercises.
By leveraging extensive datasets from previous real-time monitoring phases, DTOM in Autonomy v2 can predictively adjust TUT and rest periods based on historical data. This predictive capability allows the model to effectively customize workout plans tailored to the individual’s historical responses, maximizing the efficacy of each session without the need for live data tracking. This method ensures that training remains both personalized and efficient, optimizing muscle engagement and recovery based on past insights to drive better overall fitness results.
