Model-based Heart rate prediction during Lokomat walking.

Area of Science:

• Biomedical Engineering
• Rehabilitation Technology
• Physiology

Background:

• Current treadmill-based heart rate control models overlook significant interaction torques between the Lokomat and the patient.
• These interaction torques can substantially influence heart rate, necessitating a more comprehensive modeling approach.

Purpose of the Study:

• To implement a novel model for predicting heart rate during Lokomat-assisted walking.
• To enable prediction of potential patient overstress and facilitate adaptive physical load adjustments.

Main Methods:

• Developed a sixth-order model using walking speed and power expenditure as inputs for heart rate prediction.
• Validated the model using heart rate recordings from five distinct subjects.

Main Results:

• The implemented model accurately predicts heart rate during Lokomat walking.
• The model's inputs (walking speed, power expenditure) effectively capture key determinants of heart rate in this context.

Conclusions:

• The developed model offers a promising approach for monitoring and managing patient exertion during Lokomat rehabilitation.
• Future research will focus on model identification and predictive control for patient populations including those with spinal cord injuries and stroke.