Real-time embedded framework for force and position, estimation and control strategies for the smart prostheses

The main objective of this research is to obtain a dynamic relationship between the surface Electromyography (sEMG) signal and corresponding skeletal muscle force and joint angle, merge the data from different sensors using fusion based algorithms and feed this signal to a two stage real-time embedded controller. The proposed embedded system is capable of decoding the prerecorded surface electromyography as well as the sensory force feedback signal to control the force of the prosthetic hand prototype using a Programmable Intelligent Controller (PIC) such as 32MX360F512L. A fusion algorithm is developed to establish the correlation between the sEMG signal and the skeletal muscle force. Different control strategies: an entropy threshold value-based Proportional Integral (PI) controller, Model Reference Adaptive controller (MRAC) and optimal controller, Linear Quadratic Gaussian (LQG) controller along with a two-stage embedded design, are developed and used for the force control of the prosthetic hand. In summary, this research presents the results obtained and provides information on the proposed activities to study sEMG – skeletal muscle force and angle relationship, implementation on the test bed and controller design. This research work can also be extensible to microcontroller-based data processing and hardware architecture, an important area in computer engineering.