Jackknife: A Reliable Recognizer With Few Samples And Many Modalities

Keywords

Dynamic time warping; Gesture customization; Gesture recognition; Rapid prototyping; User evaluation

Abstract

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.

Publication Date

5-2-2017

Publication Title

Conference on Human Factors in Computing Systems - Proceedings

Volume

2017-May

Number of Pages

5850-5861

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/3025453.3026002

Socpus ID

85044868123 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85044868123

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