Hand gesture recognition is a quickly developing field with many uses in human-computer interaction, sign language recognition, virtual reality, gaming, and robotics. This paper reviews different ways to model hands, such as vision-based, sensor-based, and data glove-based techniques. It emphasizes the importance of accurate hand modeling and feature extraction for capturing and analyzing gestures. Key features like motion, depth, color, shape, and pixel values and their relevance in gesture recognition are discussed. Challenges faced in hand gesture recognition include lighting variations, complex backgrounds, noise, and real-time performance. Machine learning algorithms are used to classify and recognize gestures based on extracted features. The paper emphasizes the need for further research and advancements to improve hand gesture recognition systems’ robustness, accuracy, and usability. This review offers valuable insights into the current state of hand gesture recognition, its applications, and its potential to revolutionize human-computer interaction and enable natural and intuitive interactions between humans and machines. In simpler terms, hand gesture recognition is a way for computers to understand what people are saying with their hands. It has many potential applications, such as allowing people to control computers without touching them or helping people with disabilities communicate. The paper reviews different ways to develop hand gesture recognition systems and discusses the challenges and opportunities in this area.
This research aims to understand the enhancing reading advancement using eye gaze tracking in regards to pull the increase of time interacting with such devices along. In order to realize that, user should have a good understanding of the reading process and of the eye gaze tracking systems; as well as a good understanding of the issues existing while using eye gaze tracking system for reading process. Some issues are very common, so our proposed implementation algorithm compensate these issues. To obtain the best results possible, two mains algorithm have been implemented: the baseline algorithm and the algorithm to smooth the data. The tracking error rate is calculated based on changing points and missed changing points. In [21], a previous implementation on the same data was done and the final tracking error rate value was of 126%. The tracking error rate value seems to be abnormally high but this value is actually useful as described in [21]. For this system, all the algorithms used give a final tracking error rate value of 114.6%. Three main origins of the accuracy of the eye gaze reading were normal fixation, regression, skip fixation; and accuracies are displayed by the tracking rate value obtained. The three main sources of errors are the calibration drift, the quality of the setup and the physical characteristics of the eyes. For the tests, the graphical interface uses characters with an average height of 24 pixels for the text. By considering that the subject was approximately at 60 centimeters of the tracker. The character on the screen represents an angle of ±0.88◦; which is just above the threshold of ±0.5◦ imposed by the physical characteristics of the eyeball for the advancement of reading using eye gaze tracking.