Recognition of Hand Gesture for a Paralytic Person Using Neural Network

According to the World Health Organization, more than 17,000,000 people are infected with stroke every year in all countries around the world as a result of brain injury and prevent damage to the brain's blood supply, which leads to the injury that the patient suffers total paralysis or paraplegia a

Project Title

Recognition of Hand Gesture for a Paralytic Person Using Neural Network

Project Area of Specialization

NeuroTech

Project Summary

According to the World Health Organization, more than 17,000,000 people are infected with stroke every year in all countries around the world as a result of brain injury and prevent damage to the brain's blood supply, which leads to the injury that the patient suffers total paralysis or paraplegia and is unable to do regular activities. Muscle function in a paralyzed person's body is lost. It occurs when there is a problem with the transmission of messages between the brain and the muscles in the body. To assist stroke patients in recovering from their condition, we devised a hand gesture or sign that will enable them to conduct daily tasks more readily and communicate with normal people. If a paralyzed patient want to eat or do something else, the system assists him in doing so. This method is a useful way to assist stroke patients with what they require, such as eating or saying anything, when the patient is unable to walk due to a stroke and has complete paralysis, save for his hands. Hand Gesture (HG) has evolved into a viable alternative to traditional input devices such as a mouse and keyboard. The basic goal is to use high-resolution cameras to read and recognize hand gestures, and then process the image using a convolutional neural network for edge recognition. Convolutional neural networks are used to construct the proposed model (CNN).

Project Objectives

The goal of this project is to use Neural Networks to create a hand gesture detection system for crippled people. This is significant because the system will allow paralyzed people to speak with normal people, allowing them to carry out their everyday activities more readily. Without the technology, communication becomes difficult, if not impossible.

A hand gesture recognition system for paraplegic people will be developed to accomplish this.

As a result, it will allow paralyzed people to converse with regular people, perform daily functions, and improve their quality of life.

Project Implementation Method

Humans can read and interpret body language and sign language. Hand gestures are a type of communication in which the movement of the hand is acknowledged. The purpose of hand movement recognition of a paralytic person can be done through edge detection.

In this approach, we can quickly distinguish a paralyzed person's hand gesture. Because motions change between people and for the same individual depending on different situations, hand signals are considered a problem. It records numerous hand parameters and delivers data for analysis and system monitoring. It is dependent on the patient's motions and language interpreter. Lightning conditions and swift hand developments are the challenges in this innovation. Hand movement identification in practice necessitates the use of technologies such as high-resolution cameras to track hand movements. We must deal with a variety of issues during this process, including recognizing motions, lighting changes, hand movement, a complex background, and self-occlusions. A convolution neural network is used to recognize a paralyzed person's hand movements, with the system being trained by both the input and output based on edge detection. A real-time dataset obtained from the high-resolution cameras is used to train the system. The input from the high-resolution cameras is compared to the system's training set. Due to its reliance on monitoring patients in several places, this system has a wide range of capabilities. If a paralyzed patient want to eat or do something else, the system assists him in doing so. This method is the simplest way to assist patients and provide what they require when the patient is unable to walk due to a stroke and is completely paralysed except for his hands. The system then becomes fully reliant on hand gestures. The system's specifications include a digital camera that is linked to the active system to keep a close eye on the SP. The system's purpose is to keep track of the patient's hands.

Benefits of the Project

Advances in neurophysiology have revealed a number of systems that govern the flow and processing of information in the human brain. Some of these systems were theoretically characterized, allowing the development of computing algorithms that imitate the most fundamental of brain structures: the neuron, albeit in a simplified manner.

This paralyzed person hand gesture recognition technology will allow paralyzed people to communicate with normal people more simply, allowing them to conduct daily tasks more quickly and making their lives easier.

Technical Details of Final Deliverable

In the system, Hand movement identification in practice necessitates the use of technologies such as high-resolution cameras to track hand movements. We must deal with a variety of issues during this process, including recognizing motions, lighting changes, hand movement, a complex background, and self-occlusions. A convolution neural network is used to recognize a paralyzed person's hand movements, with the system being trained by both the input and output based on edge detection. A real-time dataset obtained from the high-resolution cameras is used to train the system. The input from the high-resolution cameras is compared to the system's training set. Due to its reliance on monitoring patients in several places, this system has a wide range of capabilities. If a paralyzed patient want to eat or do something else, the system assists him in doing so. The system then becomes fully reliant on hand gestures. The system's specifications include a digital camera that is linked to the active system through emotive headset to keep a close eye on the SP. The system's purpose is to keep track of the patient's hands.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Health

Other Industries

Core Technology

NeuroTech

Other Technologies

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

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