Sensor networks as video game input devices

Authors:
Anthony Whitehead;Nick Crampton;Kaitlyn Fox;Hannah Johnston
Affiliations:
Carleton University, Ottawa, Ontario, Canada;Carleton University, Ottawa, Ontario, Canada;Carleton University, Ottawa, Ontario, Canada;Carleton University, Ottawa, Ontario, Canada
Venue:
Future Play '07 Proceedings of the 2007 conference on Future Play
Year:
2007

Citing 5
Cited 6

Activity and Location Recognition Using Wearable Sensors

IEEE Pervasive Computing
WearNET: A Distributed Multi-sensor System for Context Aware Wearables

UbiComp '02 Proceedings of the 4th international conference on Ubiquitous Computing
Multi-Sensor Context Aware Clothing

ISWC '02 Proceedings of the 6th IEEE International Symposium on Wearable Computers
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Gait analyzer based on a cell phone with a single three-axis accelerometer

Proceedings of the 8th conference on Human-computer interaction with mobile devices and services

Classifying input for active games

Proceedings of the International Conference on Advances in Computer Enterntainment Technology
Towards customizable games for stroke rehabilitation

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Exergame effectiveness: what the numbers can tell us

Proceedings of the 5th ACM SIGGRAPH Symposium on Video Games
Activate your GAIM: a toolkit for input in active games

Futureplay '10 Proceedings of the International Academic Conference on the Future of Game Design and Technology
Homogeneous accelerometer-based sensor networks for game interaction

Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
Exploring & designing tools to enhance falls rehabilitation in the home

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work we are motivated by creating a network of sensors that can be used as input devices for video games. Our goal is to create an inexpensive network of off-the-shelf sensors that are used to force proper movement and engagement of the player. Our experience shows that a distributed set of sensors around the body prevents the player from cheating the system by using motion of the device alone to trick the system. In this work we show that a relatively simple sensor network configuration can enforce proper form and ensure that the player is actively participating in the game context.