Attentive Learning of Sequential Handwriting Movements: A Neural Network Model

Defense Advanced research Projects Agency and the Office of Naval Research (N00014-95-1-0409, N00014-92-J-1309); National Science Foundation (IRI-97-20333); National Institutes of Health (I-R29-DC02952-01)

Gesture recognition using a marionette model and dynamic bayesian networks (dbns

Abstract. This paper presents a framework for gesture recognition by modeling a system based on Dynamic Bayesian Networks (DBNs) from a Marionette point of view. To incorporate human qualities like anticipation and empathy inside the perception system of a social robot remains, so far an open issue. It is our goal to search for ways of implementation and test the feasibility. Towards this end we started the development of the guide robot ’Nicole ’ equipped with a monocular camera and an inertial sensor to observe its environment. The context of interaction is a person performing gestures and ’Nicole ’ reacting by means of audio output and motion. In this paper we present a solution to the gesture recognition task based on Dynamic Bayesian Network (DBN). We show that using a DBN is a human-like concept of recognizing gestures that encompass the quality of anticipation through the concept of prediction and update. A novel approach is used by incorporating a marionette model in the DBN as a trade-off between simple constant acceleration models and complex articulated models.

Modeling kinematics and dynamics of human arm movements.

Contains fulltext : 57338.pdf (author's version ) (Closed access)A central problem in motor control relates to the coordination of the arm's many degrees of freedom. This problem concerns the many arm postures (kinematics) that correspond to the same hand position in space and the movement trajectories between begin and end position (dynamics) that result in the same arm postures. The aim of this study was to compare the predictions for arm kinematics by various models on human motor control with experimental data and to study the relation between kinematics and dynamics. Goal-directed arm movements were measured in 3-D space toward far and near targets. The results demonstrate that arm postures for a particular target depend on previous arm postures, contradicting Donders's law. The minimum-work and minimum-torque-change models, on the other hand, predict a much larger effect of initial posture than observed. These data suggest that both kinematics and dynamics affect postures and that their relative contribution might depend on instruction and task complexity