Feedback control of heart rate during robotics-assisted treadmill exercise.
Robotics-assisted treadmill exercise has potential for cardiovascular rehabilitation of patients with miscellaneous neurological deficits. A novel approach is presented here which suggests using heart rate to define and control exercise intensity during robotics-assisted treadmill exercise. The work delineates the design and provides technical validation of the new method. A feedback structure in conjunction with a human-in-the-loop feedback for volitional control of mechanical work rate is proposed which provides automatic regulation of heart rate. A controller computes the target mechanical work rate based on target and actual heart rates. An analytical model-based method was used to design the controller. The overall feedback design process is technically validated through a test series with different control tasks including square-wave tracking, disturbance rejection, ramp tracking and an open loop test. The feedback method and the heart rate control provide close to nominal performance for square-wave and ramp reference tracking tasks below and above the anaerobic threshold, which was estimated by the V-slope method. The controllers provide robust and stable performance as verified by calculation of the root mean square error of the tracked heart rate at different effort levels as well as with the disturbance test. Further work is required to evaluate the robustness of the approach across a group of subjects including neurological patients to show the potential for clinical implementation and to achieve a positive effect for the cardiovascular status of patients.
Department of Mechanical Engineering, Bern University of Applied Sciences, Burgdorf, Switzerland.
Matthias
M
Schindelholz
Kenneth J
KJ
Hunt
D000072038:Cardiac Rehabilitation; D002318:Cardiovascular Diseases / Q000503:physiopathology; D005080:Exercise Test; D025461:Feedback, Physiological; D006339:Heart Rate; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D008991:Monitoring, Physiologic / Q000379:methods; D012119:Respiration; D012371:Robotics / Q000295:instrumentation*