A novel human computer interface based on hand gesture recognition using computer vision techniques

Over the past few years, Deep learning (DL) has revolutionized the field of data analysis. Not only are the algorithmic paradigms changed, but also the performance in various classification and prediction tasks has been significantly improved with respect to the state-of-the-art, especially in the area of computer vision. The progress made in computer vision has produced a spillover in many other domains, such as biomedical engineering. Some recent works are directed towards surface electromyography (sEMG) based hand gesture recognition, often addressed as an image classification problem and solved using tools such as Convolutional Neural Networks (CNN). This paper extends our previous work on the application of the Hilbert space-filling curve for the generation of image representations from multi-electrode sEMG signals, by investigating how the Hilbert curve compares to the Peano- and Z-order space-filling curves. The proposed space-filling mapping methods are evaluated on a variety of network architectures and in some cases yield a classification improvement of at least 3%, when used to structure the inputs before feeding them into the original network architectures.

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Computer Vision Based Hand Gesture Recognition For Speech Disabled Persons

Journal of the Institute of Engineering ◽

10.3126/jie.v11i1.14693 ◽

2016 ◽

Vol 11 (1) ◽

pp. 30-35

Author(s):

Manoj Acharya ◽

Dibakar Raj Pant

Keyword(s):

Neural Network ◽

Computer Vision ◽

Sign Language ◽

Gesture Recognition ◽

Disabled Persons ◽

Hand Gesture Recognition ◽

Hand Gesture ◽

Dependency Analysis ◽

Hand Gestures

This paper proposes a method to recognize static hand gestures in an image or video where a person is performing Nepali Sign Language (NSL) and translate it to words and sentences. The classification is carried out using Neural Network where contour of the hand is used as the feature. The work is verified successfully for NSL recognition using signer dependency analysis. Journal of the Institute of Engineering, 2015, 11(1): 30-35

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Development of Hand Gesture Based Electronic Key Using Microsoft Kinect

MATEC Web of Conferences ◽

10.1051/matecconf/201821802014 ◽

2018 ◽

Vol 218 ◽

pp. 02014

Author(s):

Arief Ramadhani ◽

Achmad Rizal ◽

Erwin Susanto

Keyword(s):

Computer Vision ◽

Gesture Recognition ◽

Recognition System ◽

Hand Gesture Recognition ◽

Microsoft Kinect ◽

Hand Gesture ◽

Depth Sensor ◽

Average Accuracy ◽

Recognition Result ◽

Hand Image

Computer vision is one of the fields of research that can be applied in a various subject. One application of computer vision is the hand gesture recognition system. The hand gesture is one of the ways to interact with computers or machines. In this study, hand gesture recognition was used as a password for electronic key systems. The hand gesture recognition in this study utilized the depth sensor in Microsoft Kinect Xbox 360. Depth sensor captured the hand image and segmented using a threshold. By scanning each pixel, we detected the thumb and the number of other fingers that open. The hand gesture recognition result was used as a password to unlock the electronic key. This system could recognize nine types of hand gesture represent number 1, 2, 3, 4, 5, 6, 7, 8, and 9. The average accuracy of the hand gesture recognition system was 97.78% for one single hand sign and 86.5% as password of three hand signs.

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