Observation of a finger or an object movement primes imitative responses differentially

Behavioural advantages for imitation of human movements over movements instructed by other visual stimuli are attributed to an ‘action observation-execution matching’ (AOEM) mechanism. Here, we demonstrate that priming/exogenous cueing with a videotaped finger movement stimulus (S1) produces specific congruency effects in reaction times (RTs) of imitative responses to a target movement (S2) at defined stimulus onset asynchronies (SOAs). When contrasted with a moving object at an SOA of 533 ms, only a human movement is capable of inducing an effect reminiscent of ‘inhibition of return’ (IOR), i.e. a significant advantage for imitation of a subsequent incongruent as compared to a congruent movement. When responses are primed by a finger movement at SOAs of 533 and 1,200 ms, inhibition of congruent or facilitation of incongruent responses, respectively, is stronger as compared to priming by a moving object. This pattern does not depend on whether S2 presents a finger movement or a moving object, thus effects cannot be attributed to visual similarity between S1 and S2. We propose that, whereas both priming by a finger movement and a moving object induces processes of spatial orienting, solely observation of a human movement activates AOEM. Thus, S1 immediately elicits an imitative response tendency. As an overt imitation of S1 is inadequate in the present setting, the response is inhibited which, in turn, modulates congruency effects.

A smart sensing platform for the discrimination of human movement

This thesis documents the design, implementation and testing of a smart sensing platform that is able to discriminate between differences or small changes in a persons walking. The distributive tactile sensing method is used to monitor the deflection of the platform surface using just a small number of sensors and, through the use of neural networks, infer the characteristics of the object in contact with the surface. The thesis first describes the development of a mathematical model which uses a novel method to track the position of a moving load as it passes over the smart sensing surface. Experimental methods are then described for using the platform to track the position of swinging pendulum in three dimensions. It is demonstrated that the method can be extended to that of real-time measurement of balance and sway of a person during quiet standing. Current classification methods are then investigated for use in the classification of different gait patterns, in particular to identify individuals by their unique gait pattern. Based on these observations, a novel algorithm is developed that is able to discriminate between abnormal and affected gait. This algorithm, using the distributive tactile sensing method, was found to have greater accuracy than other methods investigated and was designed to be able to cope with any type of gait variation. The system developed in this thesis has applications in the area of medical diagnostics, either as an initial screening tool for detecting walking disorders or to be able to automatically detect changes in gait over time. The system could also be used as a discrete biometric identification method, for example identifying office workers as they pass over the surface.

Can diversity amongst learners improve online object tracking?

We present a novel analysis of the state of the art in object tracking with respect to diversity found in its main component, an ensemble classifier that is updated in an online manner. We employ established measures for diversity and performance from the rich literature on ensemble classification and online learning, and present a detailed evaluation of diversity and performance on benchmark sequences in order to gain an insight into how the tracking performance can be improved. © Springer-Verlag 2013.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.