Article ID Journal Published Year Pages File Type
562697 Biomedical Signal Processing and Control 2012 5 Pages PDF
Abstract

A novel approach is presented which suggests the use of human metabolic work rate to define and regulate exercise intensity during robotics-assisted treadmill training. The work describes the design and technical validation of the new method.A feedback structure is proposed which provides automatic regulation of metabolic work rate, in conjunction with an embedded feedback loop for volitional control of mechanical work rate. Human metabolic work rate was derived in real time from breath-by-breath measurements of oxygen uptake and carbon dioxide output.The results show that the feedback method provides close to nominal performance for square-wave and ramp reference tracking tasks and that good disturbance rejection properties are obtained. A collateral finding of this work is an estimate of 14.5% of the metabolic efficiency of robotics-assisted treadmill exercise.The use of feedback control of human metabolic work rate provides a direct measure of exercise intensity as perceived by the exercising human as it directly reflects the energy requirements of the working muscles. This complements previous approaches to guiding robotics-assisted treadmill training based on mechanical work rate, heart rate or oxygen uptake. The new approach based on metabolic work rate may have advantages in populations with compromised and widely varying exercise responses. This provides a new approach for driving and controlling active patient participation during robotics-assisted treadmill exercise.

► A novel method for feedback control of human metabolic work rate during robotics-assisted treadmill exercise is presented. ► The approach is technically validated using experimental data. ► For the first time, an estimate of the metabolic efficiency of robotics-assisted treadmill exercise is obtained. ► We combine techniques from exercise physiology, feedback control systems, and rehabilitation engineering. ► There is high potential for clinical application for guiding active patient participation during this form of exercise.

Related Topics
Physical Sciences and Engineering Computer Science Signal Processing
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