Dynamic bayesian networks for learning interactions between assistive robotic walker and human users

Detection of individuals intentions and actions from a stream of human behaviour is an open problem. Yet for robotic agents to be truly perceived as human-friendly entities they need to respond naturally to the physical interactions with the surrounding environment, most notably with the user. This paper proposes a generative probabilistic approach in the form of Dynamic Bayesian Networks (DBN) to seamlessly account for users attitudes. A model is presented which can learn to recognize a subset of possible actions by the user of a gait stability support power rollator walker, such as standing up, sitting down or assistive strolling, and adapt the behaviour of the device accordingly. The communication between the user and the device is implicit, without any explicit intention such as a keypad or voice.The end result is a decision making mechanism that best matches the users cognitive attitude towards a set of assistive tasks, effectively incorporating the evolving activity model of the user in the process. The proposed framework is evaluated in real-life condition. © 2010 Springer-Verlag Berlin Heidelberg

A tangible programming environment model informed by principles of perception and meaning

It is a fundamental Human-Computer Interaction problem to design a tangible programming environment for use by multiple persons that can also be individualised. This problem has its origin in the phenomenon that the meaning an object holds can vary across individuals. The Semiotics Research Domain studies the meaning objects hold. This research investigated a solution based on the user designing aspects of the environment at a time after it has been made operational and when the development team is no longer available to implement the user’s design requirements. Also considered is how objects can be positioned so that the collection of objects is interpreted as a program. I therefore explored how some of the principles of relative positioning of objects, as researched in the domains of Psychology and Art, could be applied to tangible programming environments. This study applied the Gestalt principle of perceptual grouping by proximity to the design of tangible programming environments to determine if a tangible programming environment is possible in which the relative positions of personally meaningful objects define the program. I did this by applying the Design Science Research methodology with five iterations and evaluations involving children. The outcome is a model of a Tangible Programming Environment that includes Gestalt principles and Semiotic theory; Semiotic theory explains that the user can choose a physical representation of the program element that carries personal meaning whereas the Gestalt principle of grouping by proximity predicts that objects can be arranged to appear as if linked to each other.School of ComputingPh. D. (Computer Science