Abstract
This paper presents the UVa-NTS (University of Valladolid Neuromuscular Training System), a multifunction and portable Neuromuscular Training System. The UVa-NTS is designed to analyze the voluntary control of severe neuromotor handicapped patients, their interactive response, and their adaptation to neuromuscular interface systems, such as neural prostheses or domotic applications. Thus, it is an excellent tool to evaluate the residual muscle capabilities in the handicapped. The UVa-NTS is composed of a custom signal conditioning front-end and a computer. The front-end electronics is described thoroughly as well as the overall features of the custom software implementation. The software system is composed of a set of graphical training tools and a processing core. The UVa-NTS works with two classes of neuromuscular signals: the classic myoelectric signals (MES) and, as a novelty, the myomechanic signals (MMS). In order to evaluate the performance of the processing core, a complete analysis has been done to classify its efficiency and to check that it fulfils with the real-time constraints. Tests were performed both with healthy and selected impaired subjects. The adaptation was achieved rapidly, applying a predefined protocol for the UVa-NTS set of training tools. Fine voluntary control was demonstrated to be reached with the myoelectric signals. And the UVa-NTS demonstrated to provide a satisfactory voluntary control when applying the myomechanic signals.
Highlights
There is a relevant background related to neuromuscular signals applied to RehabilitationTechnologies
We will show the static tests performed with the pattern recognition processor
Strong contraction can be near Maximum voluntary contraction (MVC) but, in order to get a comfortable operation, a lower and more sustainable non-fatiguing contraction level than MVC was preferred
Summary
There is a relevant background related to neuromuscular signals applied to RehabilitationTechnologies. There is a relevant background related to neuromuscular signals applied to Rehabilitation. Myoelectric signals (MES) have been proposed to command devices, such as robotic prostheses, or act as generic man-machine interfaces in many papers [1,2,3,4,5]. Prosthesis adaptation is a major drawback for the limb impaired [6]. Amputees usually believed that a neuromuscular prosthesis could and completely replace the functions of the biological limb. Direct interaction with a robotic prosthesis can produce unsuccessful results, cases involving children being the most sensitive ones [6]. The acquisition of myoelectric prostheses has decreased drastically. A training and adaptation process is required
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