Design of space-centered interaction using invisible and intangible spatial inputs

In this paper,we investigate methodologies to improve direct-touch interaction on invisible and intangible spatial input.We firstly discuss about the motive of looking for a new input method for whole body interaction and how it can be meaningful.We also describe the role that can play spatial interaction to improve the freedom of interaction for a user.We propose a method of spatial centered interaction using invisible and intangible spatial inputs.However,given their lack of tactile feedback and visual representation,direct touch interaction on such input can be confused.In order to make a step toward understanding causes and solutions for such phenomena,we made 2 user experiments.In the first one,we test 5 setups of helper that provide information of the location of the input by constraining the dimension it is located at.The results show that using marker on the ground and a relationship with the height of the user’s body improve significantly the locative task.In the second experiment,we create a dancing game using invisible and intangible spatial inputs and we stress the results obtained in the first experiment within this cognitively demanding context.Results show that the same setup of helper is still providing very good results in that context.

Cursor Control using Smartphone in Large Display Space

We present a novel approach as a smartphone based interface that enables the users to control the cursor position in large display space.In the proposed method, computational techniques using image processing and accelerometer processing methodology serves as the core engine for fine cursor control.Unlike previous positioning mechanisms based on image processing,the proposed system is free from restraint of movement with velocity.For the implementation of our system,we combine the block matching algorithm for optical flow with three-axis acceleration to improve an accuracy and clutch.

Science Letters:Human-centered modeling for style-based adaptive games

This letter proposes a categorization matrix to analyze the playing style of a computer game player for a shooting game genre. Our aim is to use human-centered modeling as a strategy for adaptive games based on entertainment measure to evaluate the playing experience. We utilized a self-organizing map (SOM) to cluster the player's style with the data obtained while playing the game. We further argued that style-based adaptation contributes to higher enjoyment, and this is reflected in our experiment using a supervised multilayered perceptron (MLP) network.