An intelligent prosthetic hand using hybrid actuation and myoelectric control

This thesis details the design and development of an intelligent prosthetic hand based on hybrid DC and Shape Memory Alloy (SMA) actuation and controlled by only two myoelectric sensors.
A prosthesis as a tool makes no pretence of trying to replace the lost limb physiologically but it works as an aid to help provide some of the lost functions and is an
interchangeable device worn and used as needed.
Much research has been carried out to develop artificial prosthetic hands with capabilities similar to the human hand. The human hand is a very complex grasping tool,
that can handle objects of different size, weight and shape; however, they are far from providing its manipulation capabilities. This is for many different reasons, such as active bending is limited to two or three joints and user-unfriendliness. These limitations are present in commercial prosthetic hands, together with others always complained about by patients and amputees, such as inability to provide enough grasping functionality and heavy weight. Several robotic and anthropomorphic
hands may have sufficient active degrees of freedom to allow dexterity comparable to that of the human hand. Unfortunately, they cannot be used as prostheses due to
their physical characteristic that poses several serious limitations on human-hand interaction.
Hence, the motivation for this research is to investigate the use of a hybrid actuation mechanism in the design and development of an intelligent prosthetic hand. This work highlights user-friendliness and involves a proper mechanical design with more active degrees of freedom and incorporating an intelligent control system.
A system with a finger prototype is considered. Testing through simulation and physical models reveals a number of limitations. A hybrid actuation system, to increase the finger active degrees of freedom is therefore developed, with a mechanism consisting of DC and SMA actuators. Besides, only two myoelectrodes channels (enhancing the user-friendliness of the device) are used for the system control input signal.
Two novel features are developed in the new prosthetic hand. Firstly, its hybrid actuation mechanism has the advantage of increasing the active degrees of freedom;
secondly, using only two myoelectric sensors has potential for controlling more than three patterns of fingers movements.
By using artificial neural network patterns classification technique, three and five patterns of wrist joint movement corresponding to finger movement can be recognised as more than 85% correct and furthermore, seven as 70% correct.