Executive Summary : | Prosthesis plays an important role in substituting the amputated or lost body parts. There are nearly 2 million people living with limb loss in the united sates alone [1]. Among those living with limb loss ,tha main causes are vascular disease (54%)-including diabetes and peripheral arterial disease, trauma (45%) and cancer (less than 2%) [1]. The incidence of amputee in India is more than 20 millions. Moreover the incidence is higher in rural areas, and there are five to six as many male amputees as female amputees every year. Every year nearly 23,500 amputees are added to the amputee population in India[2]. Due to this high incidence and lack in proper management of these populations, there -*is certainly an economic burden to the developing country like India. In order to make the life of these disabled populations more independent, many assistive technologies have been designed. In upper and lower limb prosthesis, much noteworthy work has already been done using signals from muscle (Electromyogram) known as EMG. However, this technology has many limitations that hinder in real time application of this technology. With the advancement of new technology, brain-Machine interface promises a range of applications from welfare to warfare. The integration of physical system and the real time computational capabilities has produced various technologies like hand gesture control, mobile communication, voice based control system and smart avionics etc. In spite of recent advances in development of assistive technology for upper limb and lower limb prosthesis control using EMG, the real time computation and control is still a big challenge. In this ocean of technological interface, Brain machine interface (BMI) is a powerful and rapidly evolving technology. In this proposed plan, an upper limb prosthesis would be built with the integration of embedded technology and multi sensors data fusion acquired through wireless Electroencephalogram (EEG) sensors and Electromyograms (EMG). The brain and body multi sensors signals will be characterized and classified through machine learning techniques corresponding to various sensory-motor activities. The computations will be carried out in real time and then the physical systems will be controlled for the desired applications. |