An HMM-Based Threshold Model Approach for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning visual behavior for gesture analysis
ISCV '95 Proceedings of the International Symposium on Computer Vision
An HMM-Based Approach for Gesture Segmentation and Recognition
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Recognition of dietary activity events using on-body sensors
Artificial Intelligence in Medicine
Gesture Recognition with a 3-D Accelerometer
UIC '09 Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing
uWave: Accelerometer-based personalized gesture recognition and its applications
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Adaptive Activity Spotting Based on Event Rates
SUTC '10 Proceedings of the 2010 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing
Online Gesture Spotting from Visual Hull Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond recognition: using gesture variation for continuous interaction
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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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.