Abstract-Motion of the carotid artery wall is important for the quantification of arterial elasticity and contractility and can be estimated with a number of techniques. In this paper, a framework for quantitative evaluation of motion analysis techniques from B-mode ultrasound images is introduced. Six synthetic sequences were produced using 1) a real image corrupted by Gaussian and speckle noise of 25 and 15 dB, and 2) the ultrasound simulation package Field II. In both cases, a mathematical model was used, which simulated the motion of the arterial wall layers and the surrounding tissue, in the radial and longitudinal directions. The performance of four techniques, namely optical flow (OF H S ), weighted least-squares optical flow (OF L K (W L S ) ), block matching (BM), and affine block motion model (ABMM), was investigated in the context of this framework. The average warping indices were lowest for OF L K (W L S ) (1.75 pixels), slightly higher for ABMM (2.01 pixels), and highest for BM (6.57 pixels) and OF H S (11.57 pixels). Due to its superior performance, OF L K (W L S ) was used to quantify motion of selected regions of the arterial wall in real ultrasound image sequences of the carotid artery. Preliminary results indicate that OF L K (W L S ) is promising, because it efficiently quantified radial, longitudinal, and shear strains in healthy adults and diseased subjects.

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Abstract-This paper explores learning of interaction force skills by human demonstration in dynamic interaction tasks. Skillful force regulation is required in many cases to achieve the goal of a task and at the same time, not to cause undesired stress on the manipulator or the object under manipulation which could result in physical failure. For example, manipulation of compliant objects with varying physical properties or artistic tasks such as engraving require skillful force modulation. Humans gracefully manipulate objects by using their sense of touch and skillfully regulating exerted forces. To learn the demonstrated force for a task by demonstration, an interaction force control policy, in terms of a goal-directed dynamical system, is proposed which stems from the parallel force/position control. The control policy is parameterized and its parameters are learned by Locally Weighted Regression from human demonstrated data to learn a force trajectory. Scaling of learned force is possible by modifying the goal of the system. The proposed method is evaluated in virtual manipulation tasks using a two degrees-of-freedom haptic device.

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