Analysis of Human Computer Interaction (HCI) Model in SMEs

In Small and Medium-sized Enterprises (SMEs), Human-Computer Interaction (HCI) is considered as a cross-disciplinary segment applied in ergonomics, psychology and the engineering departments. HCI deals with the evaluation, implementation, designing and theoretical evaluation of means in which humans utilize and relate with computing applications. The term ‘Interaction’ is differentiated from other terminologies in the same application interface. The term refers to the abstract system which allows humans to interact with devices of computing for a particular industrial task. An application interface in this case applies to the selection of the technical (software and hardware) realization of a specified interaction system. Because of extensive research to incorporate diversified HCI into an understandable model, this paper evaluates HCI model in SMEs to provide the projected guidance to designers of the system using Information Technology (IT). The choice of a good model provides the recommendable direction for presentation languages e.g., Task Action Grammar (TAG) and the design actions determine the feel and look of the system. In this contribution, critical design projects in every discipline are identified alongside the present study trends and future research directions.

Gesture Based Training of Robots for Manufacturing Tasks

We report our work-in-progress on a new method to train an industrial robot that can learn from the demonstrations of manufacturing tasks by a skilled worker (trainer). A parametrized learning engine is trained based on identifiable features of the trainer’s body and objects. To achieve this, we collected a large number of depth data. Different objects in the scene are clustered using Gaussian mixture model and are manually labelled. Features are engineered to train random decision forest. Feature engineering is required since the number of dimensions (number of depth points) of samples vary because of variations in depth capture. Depth samples are transformed to a lower dimension space of 96 dimensions defined by means and covariance of data distribution. This method has a classification accuracy of 80.72%. Using these features, the robot can identify parts in real-time, tag as well as track them as the trainer moves them during the demonstration. Our ongoing work is on semantic classification of the tracked data into high level actions which will be combined using a set of rules called action-grammar.

Perspectives on object manipulation and action grammar for percussive actions in primates

The skill of object manipulation is a common feature of primates including humans, although there are species-typical patterns of manipulation. Object manipulation can be used as a comparative scale of cognitive development, focusing on its complexity. Nut cracking in chimpanzees has the highest hierarchical complexity of tool use reported in non-human primates. An analysis of the patterns of object manipulation in naive chimpanzees after nut-cracking demonstrations revealed the cause of difficulties in learning nut-cracking behaviour. Various types of behaviours exhibited within a nut-cracking context can be examined in terms of the application of problem-solving strategies, focusing on their basis in causal understanding or insightful intentionality. Captive chimpanzees also exhibit complex forms of combinatory manipulation, which is the precursor of tool use. A new notation system of object manipulation was invented to assess grammatical rules in manipulative actions. The notation system of action grammar enabled direct comparisons to be made between primates including humans in a variety of object-manipulation tasks, including percussive-tool use.

Generative Evolutionary Action Grammar Based Form Exploration

The paper describes a prototype design system based on genetic algorithm to evolve concept designs, which are coded in an action grammar. The action grammar captures the design intents expressed by the designers’ strokes during sketching process. The model is based on the assumption that the strokes made by the designers embody the aesthetic intentions expressed through the product form. The knowledge about the design generation is captured and used by the artificial neural network and genetic algorithms respectively. Principal component analysis s is used for the tacit knowledge extraction in the form of heuristics. The proposed formalism is able to support the design within a family as well creative design. The model shows the promise of a conceptual design support system for the exploration of the form during early stages of the design.

Organizing principle in 4- to 8-year-old children's drawings of embedded geometric shapes

This study looks at the way children aged 4 to 8 organize the execution of three drawings composed of simple geometric shapes (a circle, a rectangle or square, a triangle, and a diamond). In the first two experiments, we analyze the execution process used to copy and draw from memory, and we attempt to determine how this process evolves with age. The results showed that the “centripetal” execution order, where subjects drew the shapes in the outside-to-inside direction, was already prevalent in 4-year-olds. The third experiment was aimed at assessing the frequency and stability of this drawing process. We superimposed transparencies of simple geometric shapes in front of the subject's eyes. The results showed that the order in which the shapes were presented had an effect on the drawing process and how it evolved with age. They also showed that the centripetal execution principle had stabilized by the age of 8. The centripetal execution principle appears to act like the syntactic rules in the action grammar that governs elementary figure drawing.