Abstract

The goal of this project is to provide a Human Computer Interaction system to resolve the problem faced by the deaf and dumb people. Since the algorithm is not designed on the base of background hand gestures, it is immune to changes in the background image. It can process a variety of hand types, identify a number of fingers, and perform tasks as needed. The key objectives were met, as stated in this paper. Real-time gesture recognition is possible with this programme. There are certain obstacles that must be addressed in the future. For human-computer interaction, hand gesture recognition is critical. The hand region is extracted from the context in our system using background subtraction. The palm and fingers are then segmented so that the fingers can be detected and recognised. Finally, a rule classifier is used to predict hand gesture labels. Experiments on a 1300 image data set show that our method works well and is very effective. Furthermore, on another data collection of hand movements, our approach outperforms a method called state of the art. Gesture recognition is one of the essential techniques to build user-friendly interfaces.

Highlights

  • Smart devices have proliferated due to the low costs and small sizes of new digital technology

  • The Internet of Things has been widely used in the medical sector, ranging from basic self-tracking of sleep and heart rate to weight sensing and redistribution for overweight patients to avoid ulcers, as well as monitoring vital signs in hospitals and alerting

  • Accelerometers and gyroscopes are used in many Internet of Things (IoT) devices, allowing for the measurement of 3dimensional linear accelerations and angular velocities

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Summary

Introduction

Smart devices have proliferated due to the low costs and small sizes of new digital technology. Accelerometers and gyroscopes are used in many IoT devices, allowing for the measurement of 3dimensional linear accelerations and angular velocities The use of these sensors to detect individual human movements, such as smoking and opening a bottle of medicine, to alter habits that contribute to negative health consequences is one of the many applications of these sensors. Quantitative descriptions known as features are used in statistical pattern recognition. A decision rule, which is a function that places certain feature vectors in one space or subset, determines the relationship between the inputs and the outputs. Classifier learning is the ability of a classifier to classify objects based on its decision rule, and the training set is the set of feature vectors (objects) inputs and corresponding classification outputs (both positive and negative results).[6-10]. All that is needed is a good heuristic or rule of thumb to arrive at a good working solution

Algorithm for Colour Segmentation
Data Obtaining and Pre-Processing Data collection
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