Hand gesture recognition using machine learning and infrared information: a systematic literature review

Currently, gesture recognition is like a problem of feature extraction and pattern recognition, in which a movement is labeling as belonging to a given class. A gesture recognition system’s response could solve different problems in various fields, such as medicine, robotics, sign language, human–co...

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Bibliographic Details
Published in:International journal of machine learning and cybernetics 2021-10, Vol.12 (10), p.2859-2886
Main Authors: Nogales, Rubén E., Benalcázar, Marco E.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Augmented reality
Cameras
Complex Systems
Computational Intelligence
Control
Digital libraries
Engineering
Feature extraction
Gesture recognition
Human-computer interface
Information retrieval
Literature reviews
Machine learning
Mathematical models
Mechatronics
Methods
Original Article
Pattern Recognition
Robotics
Sensors
Stroke
Systematic review
Systems Biology
Virtual reality
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Summary:Currently, gesture recognition is like a problem of feature extraction and pattern recognition, in which a movement is labeling as belonging to a given class. A gesture recognition system’s response could solve different problems in various fields, such as medicine, robotics, sign language, human–computer interfaces, virtual reality, augmented reality, and security. In this context, this work proposes a systematic literature review of hand gesture recognition based on infrared information and machine learning algorithms. This systematic literature review is an extended version of the work presented at the 2019 ICSE conference. To develop this systematic literature review, we used the Kitchenham methodology. This systematic literature review retrieves information about the models’ architectures, the implemented techniques in each module, the type of learning used (supervised, unsupervised, semi-supervised, and reinforcement learning), and recognition accuracy classification, and the processing time. Also, it will identify literature gaps for future research.
ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-021-01372-y