On the encoding capacity of human motor adaptation.

Primitive-based models of motor learning suggest that adaptation occurs by tuning the responses of motor primitives. Based on this idea, we consider motor learning as an information encoding procedure, that is, a procedure of encoding a motor skill into primitives. The capacity of encoding is determined by the number of recruited primitives, which depends on how many primitives are "visited" by the movement, and this leads to a rather counterintuitive prediction that faster movement, where a larger number of motor primitives are involved, allows learning more complicated motor skills.

Age-group determination of living individuals using first molar images based on artificial intelligence.

Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs.

Gaussian Process Trajectory Learning and Synthesis of Individualized Gait Motions.

This paper proposes a Gaussian process-based method for trajectory learning and generation of individualized gait motions at arbitrary user-designated walking speeds, intended to be used in generating reference motions for robotic gait rehabilitation systems. We utilize a nonlinear dimension reduction technique based on Gaussian process dynamical models (GPDMs), in which the internal dynamics is modeled as a second-order Markov process evolving in a lower-dimensional latent space. After the GPDM parameters are identified with training data obtained from gait motions of healthy subjects walking at different speeds, our method then employs Gaussian process regression (GPR) to predict the initial two states of the latent space dynamics from any arbitrary desired walking speed and the anthropometric parameters of the test subject.

Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence.

Nearest-neighbor estimators for the Kullback-Leiber (KL) divergence that are asymptotically unbiased have recently been proposed and demonstrated in a number of applications. However, with a small number of samples, nonparametric methods typically suffer from large estimation bias due to the nonlocality of information derived from nearest-neighbor statistics. In this letter, we show that this estimation bias can be mitigated by modifying the metric function, and we propose a novel method for learning a locally optimal Mahalanobis distance function from parametric generative models of the underlying density distributions.

Optimizing Tube Precurvature to Enhance Elastic Stability of Concentric Tube Robots.

Robotic instruments based on concentric tube technology are well suited to minimally invasive surgery since they are slender, can navigate inside small cavities and can reach around sensitive tissues by taking on shapes of varying curvature. Elastic instabilities can arise, however, when rotating one precurved tube inside another. In contrast to prior work that considered only tubes of piecewise constant precurvature, we allow precurvature to vary along the tube's arc length.

Toward On-line Parameter Estimation of Concentric Tube Robots Using a Mechanics-based Kinematic Model.

Although existing mechanics-based models of concentric tube robots have been experimentally demonstrated to approximate the actual kinematics, determining accurate estimates of model parameters remains difficult due to the complex relationship between the parameters and available measurements. Further, because the mechanics-based models neglect some phenomena like friction, nonlinear elasticity, and cross section deformation, it is also not clear if model error is due to model simplification or to parameter estimation errors. The parameters of the superelastic materials used in these robots can be slowly time-varying, necessitating periodic re-estimation.

Fluid Dynamic Models for Bhattacharyya-Based Discriminant Analysis.

Classical discriminant analysis attempts to discover a low-dimensional subspace where class label information is maximally preserved under projection. Canonical methods for estimating the subspace optimize an information-theoretic criterion that measures the separation between the class-conditional distributions. Unfortunately, direct optimization of the information-theoretic criteria is generally non-convex and intractable in high-dimensional spaces.

Elastic Stability of Concentric Tube Robots Subject to External Loads.

Concentric tube robots, which are comprised of precurved elastic tubes that are concentrically arranged, are being developed for many medical interventions. The shape of the robot is determined by the rotation and translation of the tubes relative to each other, and also by any external forces applied by the environment. As the tubes rotate and translate relative to each other, elastic potential energy caused by tube bending and twisting can accumulate; if a configuration is not locally elastically stable, then a dangerous snapping motion may occur as energy is suddenly released.

A Geometric Particle Filter for Template-Based Visual Tracking.

Existing approaches to template-based visual tracking, in which the objective is to continuously estimate the spatial transformation parameters of an object template over video frames, have primarily been based on deterministic optimization, which as is well-known can result in convergence to local optima. To overcome this limitation of the deterministic optimization approach, in this paper we present a novel particle filtering approach to template-based visual tracking. We formulate the problem as a particle filtering problem on matrix Lie groups, specifically the three-dimensional Special Linear group SL(3) and the two-dimensional affine group Aff(2).

Kinematic feedback control laws for generating natural arm movements.

We propose a stochastic optimal feedback control law for generating natural robot arm motions. Our approach, inspired by the minimum variance principle of Harris and Wolpert (1998 Nature 394 780-4) and the optimal feedback control principles put forth by Todorov and Jordan (2002 Nature Neurosci. 5 1226-35) for explaining human movements, differs in two crucial respects: (i) the endpoint variance is minimized in joint space rather than Cartesian hand space, and (ii) we ignore the dynamics and instead consider only the second-order differential kinematics.

Size-assortative mating and sexual size dimorphism are predictable from simple mechanics of mate-grasping behavior.

Background: A major challenge in evolutionary biology is to understand the typically complex interactions between diverse counter-balancing factors of Darwinian selection for size assortative mating and sexual size dimorphism. It appears that rarely a simple mechanism could provide a major explanation of these phenomena. Mechanics of behaviors can predict animal morphology, such like adaptations to locomotion in animals from various of taxa, but its potential to predict size-assortative mating and its evolutionary consequences has been less explored.

Natural movement generation using hidden Markov models and principal components.

Recent studies have shown that the perception of natural movements-in the sense of being "humanlike"-depends on both joint and task space characteristics of the movement. This paper proposes a movement generation framework that merges two established techniques from gesture recognition and motion generation-hidden Markov models (HMMs) and principal components-into an efficient and reliable means of generating natural movements, which uniformly considers joint and task space characteristics. Given human motion data that are classified into several movement categories, for each category, the principal components extracted from the joint trajectories are used as basis elements.

Geometric direct search algorithms for image registration.

A widely used approach to image registration involves finding the general linear transformation that maximizes the mutual information between two images, with the transformation being rigid-body [i.e., belonging to SE(3)] or volume-preserving [i.