@inproceedings{kratz2012making,
author = {Kratz, Louis and Saponas, Scott and Morris, Dan},
title = {Making Gestural Input from Arm-Worn Inertial Sensors More Practical},
booktitle = {CHI '12 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems},
year = {2012},
month = {May},
abstract = {Gestural input can greatly improve computing experiences away from the desktop, and has the potential to provide always-available access to computing. Specifically, accelerometers and gyroscopes worn on the arm (e.g., in a wristwatch) can sense arm gestures, enabling natural input in untethered scenarios. Two core components of any gesture recognition system are detecting when a gesture is occurring and classifying which gesture a person has performed. In previous work, accurate detection has required significant computation, and high-accuracy classification has come at the cost of training the system on a per-user basis. In this note, we present a gesture detection method whose computational complexity does not depend on the duration of the gesture, and describe a novel method for recognizing gestures with only a single example from a new user},
publisher = {ACM},
url = {http://approjects.co.za/?big=en-us/research/publication/making-gestural-input-arm-worn-inertial-sensors-practical/},
pages = {1747-1750},
isbn = {978-1-4503-1015-4},
edition = {CHI '12 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems},
}