Rapid recognition of dynamic hand gestures using leap motion

Chen, Yanmei; Ding, Zeyu; Chen, Yen-Lun; Wang, Xinyu

doi:10.1109/icinfa.2015.7279509

Cited by 29 publications

(14 citation statements)

References 15 publications

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“…It considers both the precision P and the recall R of the test to compute the score: (4) In the real scene, ensure good experience of the tourists in the process of human-computer interaction, gesture recognition precision P and recall R is equally important, so we use F ( =1) as the evaluation index. In the first set of experiments, the 100 operators in turn tested the gestures by Leap Motion and recorded the results (Table 1).…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…One is based on machine learning. Yanmei Chen et al [4] using SVM proposed a fast gesture recognition method. It expanded the library of recognition of 0-9 Arabic numerals and A-Z alphabet.…”

Section: Introductionmentioning

confidence: 99%

“…Leap Motion [1][2][3][4][5][6][7][8][9][10][11][12][13] is a somatosensory controller for PC and Mac released by Leap in 2013. And Kinect [14], [15] different, Leap Motion can only recognize hand movements, such as the palm, finger position, direction, angle and other information.…”

Section: Introductionmentioning

confidence: 99%

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A Non-parametric RDP Algorithm Based on Leap Motion

Zhang¹,

Liu²,

Yuan³

et al. 2017

dtcse

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Abstract.With the popularization of computer hardware and software technology, human-machine interaction technology plays an increasingly important role within the Museum field. And it received prominent attention from academia and industry. Particularly, the appearance of Leap Motion controller, man-machine interaction application is more extensive and mature. The operator by contacting fewer devices is ready for operation, without the use of external devices such as touch screen, mouse, keyboard. It makes computer interaction more responsive and convenient. In order to increase the accuracy and practicability of gesture recognition, this paper proposes a nonparametric RDP detection algorithm based on Leap Motion. And Ramer-Douglas-Peucker (the RDP) algorithm is compared. Tests show that non-parametric RDP detection methods can effectively identify signs and have good adaptability.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Non-parametric RDP Algorithm Based on Leap Motion

Zhang¹,

Liu²,

Yuan³

et al. 2017

dtcse

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show abstract

“…Developers then can assign each of the static gesture [5] variable to an item in a game and the dynamic gesture [9] variables to trigger action sequences, such as firing a gun or throwing an object. Here we introduce a concept of "gesture blending" -the smooth transition from a static object to an action sequence with that object.…”

Section: Methodsmentioning

confidence: 99%

Representation of Intractable Objects and Action Sequences in VR Using Hand Gesture Recognition

Savosin

Prakoonwit

Tian

et al. 2017

E-Learning and Games

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Abstract. We propose a novel approach on using static and dynamic gesture recognition in VR games to represent interactive objects in games, such as equipment system, weapons and handy-tools. We examine various applications of gesture recognition in games, learning, medicine and VR, including how developers currently use the bundles of HDM devices paired with hand tracking sensors. The proposed approach provides game developers with a control over recording gestures and binding them to in-game intractable objects and equipment.

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“…Another algorithm for rapid recognition of hand graphical gestures is SVM. SVM is used to recognize 100 testing samples with an average recognition rate of 82.4% [6].…”

Section: Introductionmentioning

confidence: 99%

Deep Belief Networks for Recognizing Handwriting Captured by Leap Motion Controller

Setiawan¹,

Pulungan²

2018

IJECE

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Leap Motion controller is an input device that can track hands and fingers position quickly and precisely. In some gaming environment, a need may arise to capture letters written in the air by Leap Motion, which cannot be directly done right now. In this paper, we propose an approach to capture and recognize which letter has been drawn by the user with Leap Motion. This approach is based on Deep Belief Networks (DBN) with Resilient Backpropagation (Rprop) fine-tuning. To assess the performance of our proposed approach, we conduct experiments involving 30,000 samples of handwritten capital letters, 8,000 of which are to be recognized. Our experiments indicate that DBN with Rprop achieves an accuracy of 99.71%, which is better than DBN with Backpropagation or Multi-Layer Perceptron (MLP), either with Backpropagation or with Rprop. Our experiments also show that Rprop makes the process of fine-tuning significantly faster and results in a much more accurate recognition compared to ordinary Backpropagation. The time needed to recognize a letter is in the order of 5,000 microseconds, which is excellent even for online gaming experience.

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Rapid recognition of dynamic hand gestures using leap motion

Cited by 29 publications

References 15 publications

A Non-parametric RDP Algorithm Based on Leap Motion

A Non-parametric RDP Algorithm Based on Leap Motion

Representation of Intractable Objects and Action Sequences in VR Using Hand Gesture Recognition

Deep Belief Networks for Recognizing Handwriting Captured by Leap Motion Controller

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