Despite decades of research, there is yet no general rapid prototyping
recognizer for dynamic gestures that can be trained
with few samples, work with continuous data, and achieve high
accuracy that is also modality-agnostic. To begin to solve this
problem, we describe a small suite of accessible techniques
that we collectively refer to as the Jackknife gesture recognizer.
Our dynamic time warping based approach for both segmented
and continuous data is designed to be a robust, go-to method
for gesture recognition across a variety of modalities using
only limited training samples. We evaluate pen and touch,
Wii Remote, Kinect, Leap Motion, and sound-sensed gesture
datasets as well as conduct tests with continuous data. Across
all scenarios we show that our approach is able to achieve high
accuracy, suggesting that Jackknife is a capable recognizer
and good first choice for many endeavors.
Jackknife: A Reliable Recognizer with Few Samples and Many Modalities
Eugene M. Taranta II, Amirreza Samiei, Mehran Maghoumi, Pooya Khaloo, Corey R. Pittman, Joseph J. LaViola Jr.
Received SIGCHI Best of CHI Honorable Mention Award (top 5%).
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