SynergyFit energy recovery framework for human-centric power generation in multi-modal fitness environments

The demand for sustainable energy solutions has driven interest in capturing power from physical activity in fitness settings. This study introduces the SynergyFit Energy Recovery Model, a comprehensive theoretical framework for estimating and optimizing energy recovery across resistance, cardiovascular, and functional training. The model addresses the challenge of effectively harvesting energy in multi-modal exercise environments by identifying distinct recovery patterns: resistance training provides steady yields through consistent force application, while cardiovascular and functional training scale more rapidly with duration due to dynamic movement profiles. Validation against reported energy recovery values confirms the model's strong predictive robustness and alignment with established trends. For a training session with an average mass (plate and trainer) of 80 kg, the energy recovery results are as follows: Resistance training yields energy recoveries from 9324 J at 1.5 min to 4827.76 J at 5.0 min; Cardio training shows a marked increase, with energy recoveries ranging from 860.82 J at 1.5 min to 31,882.50 J at 5.0 min; Functional training produces energy recoveries from 466.93 J at 1.5 min to 7130.64 J at 5.0 min. The total energy recovery across these modalities ranges from 10,651.76 J to 43,840.90 J, depending on the exercise duration. The sensitivity analysis further demonstrates that increasing exercise mass, higher cardio training velocities, and longer resting periods significantly enhance energy recovery. Notably, cardio training exhibits the highest energy recoverable potential, with cumulative recovery reaching approximately 43.8 kJ per session. These insights highlight the critical role of optimizing exercise duration and parameters to maximize energy recovery. Overall, the novel SynergyFit Energy Recovery Model offers a significant advancement in understanding and improving energy recovery in fitness environments. By integrating multiple exercise modes, this model contributes to more efficient energy utilization and underscores the benefits of parameter optimization for enhanced sustainability in fitness facilities.