Numeral Gesture Recognition Using Leap Motion Sensor

Sharma, Jayash Kumar; Gupta, Rajeev; Pathak, Vinay Kumar

doi:10.1109/cicn.2015.86

Cited by 13 publications

(3 citation statements)

References 6 publications

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“…In 2015, Sharma et al [ 122 ] proposed a method for number recognition: The user moves their hand in front of the LMC and based on the form of the gesture, a number is created by the application. Before testing, they taught five sample gestures per number.…”

Section: Discussion: Accuracy and Precisionmentioning

confidence: 99%

Suitability of the Kinect Sensor and Leap Motion Controller—A Literature Review

Guzsvinecz

Szucs

Sik-Lányi

2019

Sensors

119

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As the need for sensors increases with the inception of virtual reality, augmented reality and mixed reality, the purpose of this paper is to evaluate the suitability of the two Kinect devices and the Leap Motion Controller. When evaluating the suitability, the authors’ focus was on the state of the art, device comparison, accuracy, precision, existing gesture recognition algorithms and on the price of the devices. The aim of this study is to give an insight whether these devices could substitute more expensive sensors in the industry or on the market. While in general the answer is yes, it is not as easy as it seems: There are significant differences between the devices, even between the two Kinects, such as different measurement ranges, error distributions on each axis and changing depth precision relative to distance.

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Section: Discussion: Accuracy and Precisionmentioning

confidence: 99%

Suitability of the Kinect Sensor and Leap Motion Controller—A Literature Review

Guzsvinecz

Szucs

Sik-Lányi

2019

Sensors

119

View full text Add to dashboard Cite

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“…13 Sharma et al used Leap Motion combined with geometric template matching method to identify 10 numbers and achieved a recognition rate of 70.2%. 14 Xintong et al used Leap Motion and HTC VIVE to achieve virtual maintenance demonstration of aircraft electronic equipment by touching a virtual object, but it lacked training for real disassembly and assembly actions. 15 At present, virtual maintenance technology based on gesture interaction has been applied to the development of aircraft maintenance manuals.…”

Section: Related Workmentioning

confidence: 99%

AGRMTS: A virtual aircraft maintenance training system using gesture recognition based on PSO‐BPNN model

Yan

Zhang

Min-ye

2021

Computer Animation & Virtual

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The quality and efficiency of aircraft maintenance are the key to ensure flight safety and on-time rate, which mainly depend on the techniques and experience of maintenance engineer. Generally, exercises on physical prototypes are used to improve the maintenance capability of engineers, but this will waste a lot of consumables and easily cause safety accidents. With the development of computer technology, maintenance training in a virtual environment has become an advanced and reliable solution. In this paper, a virtual training system of aircraft maintenance based on gesture recognition interaction is established. Leap Motion is used as a sensor to construct a hybrid machine learning gesture recognition model, so as to obtain natural human-computer interaction experience.In the recognition model, the initial weight matrix and the number of hidden layer nodes in the back propagation neural network are jointly optimized by the Particle Swarm Optimization algorithm with self-adaption inertial weight. This optimization algorithm achieved a recognition rate of 81.26% in the dynamic gesture database constructed in this paper, which is higher than other available algorithms. A preliminary usability evaluation in university classrooms shows that the teaching system in this paper can achieve a better interactive experience.

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“…Since that times, the gesture recognition field has been extensively studied, particularly the hand gesture recognition and facial recognition [6,10]. The gesture recognition has been applied to solve various problems in human-computer interaction field, including 'serious games' and rehabilitation applications, handwriting, numeral gesture recognition and Sign Language [12,14,16].…”

Section: Introductionmentioning

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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Numeral Gesture Recognition Using Leap Motion Sensor

Cited by 13 publications

References 6 publications

Suitability of the Kinect Sensor and Leap Motion Controller—A Literature Review

Suitability of the Kinect Sensor and Leap Motion Controller—A Literature Review

AGRMTS: A virtual aircraft maintenance training system using gesture recognition based on PSO‐BPNN model

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

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