Аңдатпа
This paper presents the development of a hardware architecture for a control system of an upper-limb bionic prosthesis based on surface electromyography (EMG). The proposed system is intended for acquisition, amplification, filtering, and digitization of bioelectrical signals generated by skeletal muscles to provide reliable input data for prosthetic control algorithms. The designed hardware architecture includes an instrumentation amplifier for acquiring low-amplitude EMG signals, an active common-mode interference suppression circuit to reduce external noise, an active band-pass filter with a passband of 60–500 Hz to isolate the informative frequency range of the EMG signal, an adjustable gain stage, and a high-resolution sigma–delta analog-to-digital converter (ADC) for signal digitization. The relevance of the proposed solution is driven by the increasing need for advanced prosthetic and rehabilitation technologies for civilian patients and military personnel with upper-limb amputations resulting from trauma and combat-related injuries. The developed hardware architecture provides a reliable platform for biosignal acquisition and processing in rehabilitation-oriented prosthetic systems, supporting restoration of motor functions and improvement of functional independence. To evaluate the performance of the proposed architecture, circuit modeling was performed in the Multisim simulation environment. Real EMG recordings obtained from the PhysioNet biomedical signal database were used as input signals. In addition, synthetic disturbances, including 50 Hz power-line interference and low-frequency respiratory artifacts, were introduced to simulate realistic operating conditions. The simulation results demonstrated effective suppression of undesirable harmonics while preserving informative spectral components of the EMG signal. A component-level analysis was conducted considering parameters such as input impedance, noise characteristics, power consumption, and physical dimensions. The developed architecture is characterized by modularity, adjustable amplification capability, energy efficiency, and scalability for multi-channel implementations. The proposed solution can be applied in development of autonomous upper-limb bionic prostheses for medical rehabilitation of civilian patients and military personnel and can serve as a hardware platform for integration of intelligent control algorithms in advanced prosthetic systems.