Evidence for Composite Cost Functions in Arm Movement Planning: An Inverse Optimal Control Approach

An important issue in motor control is understanding the basic principles underlying the accomplishment of natural movements. According to optimal control theory, the problem can be stated in these terms: what cost function do we optimize to coordinate the many more degrees of freedom than necessary to fulfill a specific motor goal? This question has not received a final answer yet, since what is optimized partly depends on the requirements of the task. Many cost functions were proposed in the past, and most of them were found to be in agreement with experimental data. Therefore, the actual principles on which the brain relies to achieve a certain motor behavior are still unclear. Existing results might suggest that movements are not the results of the minimization of single but rather of composite cost functions. In order to better clarify this last point, we consider an innovative experimental paradigm characterized by arm reaching with target redundancy. Within this framework, we make use of an inverse optimal control technique to automatically infer the (combination of) optimality criteria that best fit the experimental data. Results show that the subjects exhibited a consistent behavior during each experimental condition, even though the target point was not prescribed in advance. Inverse and direct optimal control together reveal that the average arm trajectories were best replicated when optimizing the combination of two cost functions, nominally a mix between the absolute work of torques and the integrated squared joint acceleration. Our results thus support the cost combination hypothesis and demonstrate that the recorded movements were closely linked to the combination of two complementary functions related to mechanical energy expenditure and joint-level smoothness

Human Genome behaviour: a powerful mechanism for optimising the use of Satellite Technology in Surveying Networks Design

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Marker-free Registration for Electromagnetic Navigation Bronchoscopy under Respiratory Motion

Abstract. Electromagnetic navigation bronchoscopy requires the accurate registration of a preinterventional computed tomography (CT) image to the coordinate system of the electromagnetic tracking system. Current state-of-the-art registration methods are manual or do not explicitly take patient’s respiratory motion and exact airway shape into account, leading to relatively low accuracy. This paper presents an automated registration method addressing these issues. Electromagnetic tracking data recorded during bronchoscopic examination is matched to the airways by an optimizer utilizing the Euclidean distance map to the centerline of the airways for automated registration. Using a cutaneous sensor on the chest of the patient allows us to approximate respiratory motion by a linear deformation model and adopt the registration result in real time to the current respiratory phase. A thorough in silico evaluation on real patient data including CT images taken in 10 respiratory phases shows the significant registration error decrease of our method compared to the current state of the art, reducing the error from 3.5 mm to 2.8 mm.