Name
Abstract | ||
|---|---|---|
The key aspect that defines the experience when playing a video game is the effectiveness and intuitiveness of gameplay, allowing an unexperienced player to quickly learn the main aspects of an interactive game and start playing immediately. While older games were usually simple and could be operated with 1 or 2 buttons with a quick learning curve, modern games allow a wide variety of actions that demands a more complex control scheme, sometimes with 10 or 15 buttons, resulting in unintuitive controls. Other methods of interaction, like touch, motion controls and voice, presented a more intuitive way to play games, but never reached the same level of precision found in regular controllers. To create an easier way to interact with games but at the same time, maintain the precision and quick response, delivering the best from both worlds, this work proposes the AdaptControl: a virtual controller based on an Android touchscreen device that communicates to a PC and works as a regular joystick to control a game, that can display only the amount of buttons needed for a game in a simplified interface. But this flexibility creates another challenge: the lack of physical feedback to the user. To solve this issue, the AdaptControl uses machine learnings algorithms to detect when the user is missing buttons and correct its position and size to an optimal configuration. And this kind of intelligence applied to the controller will bring another benefit: despite starting with a generic configuration for one game, the controller will be capable of changing its own layout to match each users' ergonomic need, resulting in a personal controller that matches the player's needs. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2669062.2669081 | SIGGRAPH Asia 2014 Mobile Graphics and Interactive Applications |
Field | DocType | Citations |
Computer vision,Control theory,Android (operating system),Computer graphics (images),Computer science,Touchscreen,Artificial intelligence,Joystick,Multimedia | Conference | 3 |
PageRank | References | Authors |
0.44 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Leonardo Torok | 1 | 16 | 3.14 |
| Mateus Pelegrino | 2 | 8 | 1.95 |
| Jefferson Lessa | 3 | 4 | 0.80 |
| Daniela Gorski Trevisan | 4 | 29 | 10.14 |
| Esteban Walter Gonzalez Clua | 5 | 5 | 0.87 |