Personalized heart rate management through data-driven dynamic exercise control

Area of Science:

• Exercise Physiology
• Control Systems Engineering
• Biomedical Engineering

Background:

• Maximizing healthy life expectancy requires optimal exercise intensity for health and rehabilitation.
• Heart rate monitoring is key to controlling exercise intensity.
• Individual variations in heart rate dynamics necessitate personalized control parameters.

Purpose of the Study:

• To develop a data-driven method for optimizing proportional-integral (PI) control parameters for heart rate regulation during exercise.
• To enable safe and effective heart rate control for improved exercise intensity management.
• To reduce the need for extensive subject exercise to model heart rate dynamics.

Main Methods:

• Utilized a proportional-integral (PI) control system to dynamically adjust bicycle ergometer load.
• Employed a data-driven design approach to optimize PI parameters directly from exercise data.
• Validated the method on fifteen healthy volunteers to assess heart rate control accuracy.

Main Results:

• Achieved approximately 40% improvement in control error for the target heart rate trajectory.
• Demonstrated effective heart rate control regardless of subject gender or age.
• Identified that females tend to exhibit higher heart rates than males at equivalent workloads.

Conclusions:

• The proposed data-driven PI parameter optimization is effective for personalized heart rate control during exercise.
• This method enhances the safety and efficacy of exercise intensity management for health and rehabilitation.
• Findings suggest potential sex-based differences in cardiovascular response to exercise load.