Characterization of bilateral reaching development using augmented reality games.

Bilateral coordination is commonly impaired in neurodevelopmental conditions including cerebral palsy, developmental coordination disorder, and autism spectrum disorder. However, we lack objective clinical assessments that can quantify bilateral coordination in a clinically feasible manner and determine age-based norms to identify impairments. The objective of this study was to use augmented reality and computer vision to characterize bilateral reaching abilities in typically developing children.

Predicting Perceived Disfigurement from Facial Function in Patients with Unilateral Paralysis.

Background: Clinical rating tools such as the electronic, clinician-graded facial function (eFACE) scale provide detailed information about aspects of facial functioning relevant to the assessment and treatment of facial paralysis. Past research has established that eFACE scores significantly relate to expert ratings of facial disfigurement. However, no studies have examined the extent to which eFACE scores relate to casual observers' perceptions of disfigurement in facial paralysis.

Dynamic properties of successful smiles.

Facial expression of emotion is a foundational aspect of social interaction and nonverbal communication. In this study, we use a computer-animated 3D facial tool to investigate how dynamic properties of a smile are perceived. We created smile animations where we systematically manipulated the smile's angle, extent, dental show, and dynamic symmetry.

A Method for Using Player Tracking Data in Basketball to Learn Player Skills and Predict Team Performance.

Player tracking data represents a revolutionary new data source for basketball analysis, in which essentially every aspect of a player's performance is tracked and can be analyzed numerically. We suggest a way by which this data set, when coupled with a network-style model of the offense that relates players' skills to the team's success at running different plays, can be used to automatically learn players' skills and predict the performance of untested 5-man lineups in a way that accounts for the interaction between players' respective skill sets. After developing a general analysis procedure, we present as an example a specific implementation of our method using a simplified network model.

Universal power law governing pedestrian interactions.

Human crowds often bear a striking resemblance to interacting particle systems, and this has prompted many researchers to describe pedestrian dynamics in terms of interaction forces and potential energies. The correct quantitative form of this interaction, however, has remained an open question. Here, we introduce a novel statistical-mechanical approach to directly measure the interaction energy between pedestrians.

Least-effort trajectories lead to emergent crowd behaviors.

Pedestrian crowds often have been modeled as many-particle systems, usually using computer models known as multiagent simulations. The key challenge in modeling crowds is to develop rules that guide how the particles or agents interact with each other in a way that faithfully reproduces paths and behaviors commonly seen in real human crowds. Here, we propose a simple and intuitive formulation of these rules based on biomechanical measurements and the principle of least effort.

Interactive navigation of heterogeneous agents using adaptive roadmaps.

We present a novel algorithm for collision-free navigation of a large number of independent agents in complex and dynamic environments. We introduce adaptive roadmaps to perform global path planning for each agent simultaneously. Our algorithm takes into account dynamic obstacles and interagents interaction forces to continuously update the roadmap based on a physically-based dynamics simulator.