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-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.