Physical activity and exercise is an important step in the journey of a healthy lifestyle. It reduces the risk of developing many diseases like diabetes, cardiovascular diseases, and even cancer. In today’s world, we spend most of our time sitting in front of a computer or mobile phones for work and entertainment. This is one of the leading factors in a lack of physical activities and thus a decrease in the fitness level of many individuals. In this paper, we design a mobile 2D platformer game where progress inside the game world is dependent on physical activity in the real world. The game has two modes namely adventure and story mode. This design can motivate people to lead an active and healthy lifestyle. The physical activity monitoring is done solely through the pedometer of a smartphone held by the user while playing the game. The number of steps walked by the user determines the number of revives inside the adventure mode of the game. There are three levels in the story mode which are unlocked by walking more than a certain number of steps in the real world.

A new near loss-less compression method for 2D game sprite animation image is presented to reduce the amount of the game contents resources. The sprite animation data in 2D mobile game occupies a large part of the resource, and in particular the sprite animation data by using a lossless compression technique can cause a relatively very significant increase of data amount as the higher complexity of the animation. The new near-lossless method, which is an adaptive compression technique by considering the alpha value for transparency and the variance of color values of block, is proposed in order to obtain greater compression efficiency without los of quality. By using experiments, we show that the proposed method compared to the PNG compression technique is good for compression efficiency, and in particular the effect is very high in complex and dynamic 2D sprite animation.

최근의 인공 신경망(Neural Network) 기법은 전통적인 분류 문제와 군집화 문제 해결에서 벗어나 이미지 생성 같은 컨텐츠 생성에서도 좋은 성능을 보이고 있다. 본 연구에서는 차세대 컨텐츠 생성 기법으로 인공신경망을 이용한 이미지 생성기법을 제안한다. 제안하는 인공신경망 모델은 두 개의 이미지를 입력받아서 하나의 이미지에서는 색상을, 다른 이미지에서는 모양을 가져와 새로운 이미지로 조합해낸다. 이 모델은 컨볼루션 인공신경망(Convolutional Neural Network)으로 제작되 었으며 각각 이미지에서 색상과 모양을 추출해내는 두 개의 인코더와 각 인코더의 값을 모두 넘겨 받아 하나의 조합이 되는 이미지를 생성해내는 하나의 디코더로 구성이 되어있다. 본 연구의 성과는 저비용으로 게임 개발 프로세스 상 다양한 2차원 이미지 생성 및 보정 작업에 활용될 수 있다.