Developing a 3D prosthetic hand can offer assistance to many individuals by getting a chance to feel alive again after getting their hand amputated for natural causes or accidents. This hand is moved by the electrical signal of the muscles (EMG) the signal of movement is recorded by connecting electrodes to the muscles of the hand and the movement is transmitted to the hand via a Micro-controller which controls the actions of motors connected with the hand by a transmission mechanism that ensures smooth and easy movement as possible However, lack of reliability in developing a 3D prosthetic hand can be an incredible issue as the prosthetic hand movement does not coordinate the normal hand developments. Moreover, it may include constrained data that ought to not be sufficient to cover everyday hand developments. These days, the prosthetic hand is exceptionally expensive to manage. On the other hand, the proposed system aims to solve as many problems by using feature extraction and machine learning classification techniques so the prosthetic hand can mimic the natural hand as much as possible.

The main objectives of the project are:

1- Provide a more reliable method to make the prosthetic hand resemble the native hand as much as possible.

2- To be more efficient and accurate to simulate reality.

3- To be as light as possible in order to help in daily use.

4- providing a much more cheaper prosthetic hand rather than the expensive ones.

The main aim of the proposed system is to create a 3D prosthetic hand using EMG signals that shall meet the following requirements. Firstly, achieving high accuracy by testing some of the classifications. Secondly,making the hand as reliable as possible. Thus, it would act the same as the natural hand. Lastly, reducing the cost so lots of people can be able to buy it.