Modeling of hyper-adaptability: from motor coordination to rehabilitation

Hyper-adaptability is an ability of humans and animals to adapt to large-scale changes in the nervous system or the musculoskeletal system, such as strokes and spinal cord injuries. Although this adaptation may involve similar neural processes with normal adaptation to usual environmental and body changes in daily lives, it can be fundamentally different because it requires ‘construction’ of the neural structure itself and ‘reconstitution’ of sensorimotor control rules to compensate for the changes in the nervous system. In this survey paper, we aimed to provide an overview on how the brain structure changes after brain injury and recovers through rehabilitation. Next, we demonstrated the recent approaches used to apply computational and neural network modeling to recapitulate motor control and motor learning processes. Finally, we discussed future directions to bridge the gap between conventional physiological and modeling approaches to understand the neural and computational mechanisms of hyper-adaptability and its applications to clinical rehabilitation

Evaluating the effect of robot group size on relative localisation precision

Looking on co-operative position estimation in multi-robot systems, the question to what extend the number of robots has an influence on the quality of the resulting localisation is an important and interesting issue. This paper addresses this relation regarding a pure relative localisation approach based only on mutual observations between the robots. The intuitive expectation that more robots should improve the position estimation is motivated and the design of the experiments with special respect to possibly distorting parameters is discussed and reasoned in detail. An in-depth analysis of the collected data explains the only partial conformance of the experimental results with the expected outcome

Pose-Invariant 3D Proximal Femur Estimation through Bi-planar Image Segmentation with Hierarchical Higher-Order Graph-Based Priors

International audienceLow-dose CT-like imaging systems offer numerous perspectives in terms of clinical application, in particular for osteoarticular diseases. In this paper, we address the challenging problem of 3D femur modeling and estimation from bi-planar views. Our contributions are threefold. First, we propose a non-uniform hierarchical decomposition of the shape prior of increasing clinical-relevant precision which is achieved through curvature driven unsupervised clustering acting on the geodesic distances between vertices. Second, we introduce a graphical-model representation of the femur which can be learned from a small number of training examples and involves third-order and fourth-order priors, while being similarity and mirror-symmetry invariant and providing means of measuring regional and boundary supports in the bi-planar views. Last but not least, we adopt an efficient dual-decomposition optimization approach for efficient inference of the 3D femur configuration from bi-planar views. Promising results demonstrate the potential of our method

Performance Evaluation of a Class of Gravity-Compensated Gear-Spring Planar Articulated Manipulators

This paper is devoted to evaluating the gravity compensation performance of a special class of planar articulated manipulators that are gravity balanced by using a series of gear-spring modules. First, the studied manipulators with one, two, and three DOFs are revisited. Then, the gravity compensation performance of these manipulators is determined via a peak-to-peak torque reduction criterion. As the manipulators were designed via two different approaches, i.e., the ideal balancing approximation and the realistic optimization, the gravity compensation performance of these two approaches is compared. It shows that the perfect balancing approximation can achieve a satisfied performance as nearly same as that of the optimization approach, while it, on the other hand, enjoys a significant reduction of the computational effort for gravity compensation design