Man-machine interface system for neuromuscular training and evaluation based on EMG and MMG signals

Area of Science:

• Biomedical Engineering
• Rehabilitation Technology
• Neuroscience

Background:

• Neuromotor impairments significantly affect voluntary muscle control.
• Assessing residual muscle capabilities is crucial for rehabilitation and assistive technology development.
• Existing systems may not adequately capture diverse neuromuscular signals or offer portable solutions.

Purpose of the Study:

• To introduce the University of Valladolid Neuromuscular Training System (UVa-NTS).
• To evaluate the system's efficacy in analyzing voluntary control in individuals with neuromotor handicaps.
• To assess the system's capability in processing both myoelectric signals (MES) and myomechanic signals (MMS).

Main Methods:

• Development of a portable, multifunction neuromuscular training system (UVa-NTS).
• Integration of custom signal conditioning front-end electronics and specialized software.
• Utilizing graphical training tools and a processing core for analyzing myoelectric signals (MES) and myomechanic signals (MMS).
• Performance evaluation through real-time constraint analysis and testing with healthy and impaired subjects.

Main Results:

• The UVa-NTS demonstrated rapid adaptation to a predefined training protocol.
• Fine voluntary control was achieved using myoelectric signals (MES).
• Satisfactory voluntary control was achieved using myomechanic signals (MMS), highlighting the system's novelty.
• The processing core met real-time performance constraints.

Conclusions:

• The UVa-NTS is an effective tool for evaluating residual muscle capabilities in patients with neuromotor handicaps.
• The system successfully facilitates voluntary control acquisition using both myoelectric and myomechanic signals.
• UVa-NTS offers a versatile and portable solution for neuromuscular rehabilitation and assistive technology research.