  This work focuses on the optimization of the training trajectory orientation
using a robot as an advanced exercise machine (AEM) and muscle activations as
biofeedback. Muscle recruitment patterns depend on trajectory parameters of the
AEMs and correlate with the efficiency of exercise. Thus, improvements to
training efficiency may be achieved by optimizing these parameters. The optimal
regulation of these parameters is challenging because of the complexity of the
physiological dynamics from person to person as a result of the unique physical
features such as musculoskeletal distribution. Furthermore, these effects can
vary due to fatigue, body temperature, and other physiological factors. In this
paper, a model-free optimization method using Extremum Seeking Control (ESC) as
a real-time optimizer is proposed. After selecting a muscle objective, this
method seeks for the optimal combination of parameters using the muscle
activations as biofeedback. The muscle objective can be selected by a therapist
to emphasize or de-emphasize certain muscle groups. The feasibility of this
method has been proven for the automatic regulation of an ellipsoidal curve
orientation, suggesting the existence of two local optimal orientations. This
methodology can also be applied to other parameter regulations using a
different physiological effects such as oxygen consumption and heart rate as
biofeedback.
