Non-Invasive Prosthetic Hand Model

Prosthetics are artificial devices that replace a missing body part, which may be lost through, disease, accidents or combat in times of warfare. The exponential advances in microprocessor technology coupled with affordability have spawned an online support community that have incorporated cost-effective microcontrollers in interesting applications. In relation to prosthetics, scientists and engineers frequent drawing boards periodically to update prosthetic design, each time incorporating more features that are increasingly less noticeable to the user. However, strict standards for medical devices and demand for reliability mean very few implementations have progressed from research and development phases to production for the commercial sector. The scientific community has been working diligently on this frontier for decades but it is still considered an open problem needing to be solved. Even though many breakthroughs are likely to occur within the next decade, the likelihood of cost effective implementation from large-scale research projects is slim meaning most amputees will not be able to afford this technology. This research is focused on the implementation a prototype of a non-invasive prosthetic hand model replicated by a robotic hand. Electromyography is the study of electrical signals produced by the movement of muscles in the human body .The prototype will be designed to use pre-processed electromyography signals as control signals in order to demonstrate replication of simple hand movements. This research project will be completed by first examining pre-processed electromyography signals identified that trigger specific hand movements and then programming microcontrollers to recognize these signals, which leads to ultimately replicating hand movements by controlling the servo motors of a robotic hand. The effective combination of hardware and software has yet to be determined. This research benefits society because it will provide insight to using cost effective hardware as a basis for prosthetic limb control thus allowing future products in the commercial sector to benefit from these savings

A Case Study on Tuning Artificial Neural Networks to Recognize Signal Patterns of Hand Motions

This paper presents the development of artificial neural networks (ANN) as pattern recognition systems to classify surface electromyography signals (sEMG) into nine select hand motions from seven subjects. Multiple networks were designed to determine how well a network could adapt to signals from different subjects. This was achieved by developing multiple networks with different combinations of the volunteers for training. Each network was tested with signals from all volunteers to determine how well they could adapt to new subjects. It was found that ANNs trained using only one or two subjects would perform exceptionally well when tested with signals from the same subjects but relatively poorly when tested with signals from new subjects. As the number of subjects used for training increased, the ability of the network to accurately classify the signals from the trainees decreased but their ability to adapt to signals from new subjects increased. Solely based on these results, it can be inferred that ANNs developed using signals from a large amount of subjects could be used to accurately classify signals from completely new subjects. Research presented in this paper has potential to be further developed as a basis for utilizing sEMG as control signals in electric devices such as myoelectric prosthesis or humanoid control