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.
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De la Rosa, R.; Alonso, A.; Carrera, A.; Durán, R.; Fernández, P. Man-Machine Interface System for Neuromuscular Training and Evaluation Based on EMG and MMG Signals. Sensors 2010, 10, 11100-11125.
De la Rosa R, Alonso A, Carrera A, Durán R, Fernández P. Man-Machine Interface System for Neuromuscular Training and Evaluation Based on EMG and MMG Signals. Sensors. 2010; 10(12):11100-11125.
De la Rosa, Ramon; Alonso, Alonso; Carrera, Albano; Durán, Ramon; Fernández, Patricia. 2010. "Man-Machine Interface System for Neuromuscular Training and Evaluation Based on EMG and MMG Signals." Sensors 10, no. 12: 11100-11125.