Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes
By Jacob O. Wobbrock, Andrew D. Wilson, Yang Li. ACM Symposium on User Interface Software and Technology, 2007
889 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/dc3982e0c29d30491715d350f792caaeeef9ede7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Jo Vermeulen
- Wendy E. Mackay
- Olivier Bau
- Saul Greenberg
- Jacob O. Wobbrock
- Jiayang Liu
- Lin Zhong
- Jehan Wickramasuriya
In Human-Computer Interaction
- Jo Vermeulen
- Wendy E. Mackay
- Olivier Bau
- Nicolai Marquardt
- Lora Oehlberg
- Steven Houben
- David Ledo
- Saul Greenberg
Citations in the corpus
- Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping (2012, methodology): “There are dozens of research efforts in this domain that explicitly state that while monitoring under DTW is desirable, it is impossible [38].”
- uWave: Accelerometer-based personalized gesture recognition and its applications (2009, methodology): ““$1 recognizer” [16], were also based on template matching, similar to uWave.”
- Evaluation Strategies for HCI Toolkit Research (2018, background): “While not a toolkit (and thus not part of our dataset), the $1 Gesture Recognizer [110] is an excellent example of benchmarking against the state-of-the-art: the benchmarks showed that it was considerably close to the state-of-the-art, yet much simpler to implement (about 100 lines of code).”
- 3D Gestural Interaction: The State of the Field (2013, neutral): “’s $1 2D recognizer [127].”
- The aligned rank transform for nonparametric factorial analyses using only anova procedures (2011, background): “[14] compared a new stroke recognizer to two published recognizers in a within-subjects study of gestures made at three different speeds by 10 participants.”
- OctoPocus: a dynamic guide for learning gesture-based command sets (2008, methodology): “Technical perspective Gesture recognition algorithms include Rubine’s popular gesture classifier [29], which requires initial training by drawing sample gestures, and the $1 Recognizer [31], which provides a simple and efficient algorithm intended to support rapid prototyping of gesture-based interf”
- 1 € filter: a simple speed-based low-pass filter for noisy input in interactive systems (2012, background): “The ‘1e’ name is an homage to the $1 recognizer [10]: we believe that the 1e filter can make filtering input signals simpler and better, much like the $1 recognizer did for gestures.”