To identify the underlying mechanisms of human motor control, parametric models are utilized. One approach of employing these models is inferring the control intent (estimating motor control strategy). A well-accepted assumption is that human motor control is optimal, thus the intent is inferred by solving an inverse optimal control problem. Linear quadratic regulator (LQR) is a well-established optimal controller and its inverse problem (ILQR) has been used in the literature to infer the control intent of one subject. This implementation used a cost function with gain penalty; minimizing the error between LQR gain and a preliminary estimated gain. We hypothesize that relying on an estimated gain may limit ILQR optimization capability. In this study, we derive an ILQR optimization with output penalty; minimizing the error between the model output and the measured output. We conducted the test on 30 healthy subjects who sat on a robotic seat capable of rotation. The task utilized a physical human-robot interaction with a perturbation torque as input and lower and upper body angles as output. Our method significantly improved the goodness of fit compared to the gain-penalty ILQR. Moreover, the dominant inferred intent was not statistically different between the two methods. To our knowledge, this work is the first that infers motor control intent for a sample of healthy subjects. This is a step closer to investigating control intent differences between healthy subjects and subjects with altered motor control, e.g., low back pain.

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