# Conception of a System-on-Chip (SoC) Platform to Enable EMG-Guided Robotic Neurorehabilitation
## 1 Introduction
This lack of improvement has been attributed to the passive involvement of the patient, who may perform the exercises without active engagement, potentially leading to mental disengagement and, consequently, a diminished capacity to enhance neuroplasticity. To overcome this limitation, the integration of electromyography (EMG) signals into exoskeleton control systems has been proposed as one of several techniques to facilitate active rehabilitation, thereby improving the overall effectiveness of EAM for rehabilitation [7; 8]. EMG is a reliable and well-established method for recording the electrical activity of muscles, enabling precise tracking of muscular activity during movement. The response range extends from 0.01 Hz to 10 kHz in bandwidth, encompassing low-frequency components often associated with physiological or mechanical artifacts, as well as high-frequency details relevant for advanced intramuscular EMG studies. This broad range provides flexibility for experimental setups and specialized applications, although the typical useful bandwidth for surface EMG lies between 20 Hz and 500 Hz. By quantifying the intensity of muscle-generated electrical signals, EMG provides valuable insights into muscle function, treatment effectiveness, and the degree of force exerted, facilitating the personalization of rehabilitation sessions [7]. This makes EMG a powerful tool for integration into therapeutic exoskeletons, as it allows the evaluation of both the patient's intent and movement force, even when the generated force is insufficient or gradually improving [9; 10; 11]. EMG-driven systems could enable exoskeletons to dynamically adjust their assistance to match the user's physical capabilities, potentially supporting real-time adaptive control that fosters personalized human-machine interaction [12; 13]. This approach may improve the effectiveness of assisted movements while encouraging active user participation, which could play a crucial role in promoting neuroplasticity and enhancing rehabilitation outcomes. Despite its advantages, using EMG in exoskeleton control systems presents challenges, primarily due to the low amplitude and noise susceptibility of signals sampled, compounded by external interference, movement artifacts, and electrode placement variations (low SNR) [7; 14]. These issues require advanced filtering and conditioning techniques. Additionally, integrating EMG-based control demands real-time signal processing with high-performance hardware and software to ensure seamless interaction and minimize latency, which is critical for user confidence and safety. Nevertheless, EMG remains invaluable for assessing force and movement intent, enabling adaptive assistance tailored to the user's needs. Advancements in sensor technology and machine learning algorithms continue to enhance the accuracy and reliability of EMG-based systems, making them fundamental to personalized rehabilitation strategies. While commercial EMG systems are well-suited for standard conditions, their sampling rates and reliance on external processing devices often limit their adaptability to advanced applications, such as real-time motion control in therapeutic exoskeletons. To address these limitations, this study proposes the development of a high-performance multichannel EMG acquisition system with a sampling rate of 8 kSPS per differential channel and real-time in situ signal processing capabilities [15]. The proposed system incorporates edge computing to locally process EMG signals, reducing latency and eliminating the need to transmit noisy raw signals. Additionally, by minimizing cable length and optimizing signal acquisition, the system mitigates electromagnetic interference and enhances portability. Designed for seamless integration into ROS2-based control architectures, the system transmits only processed data to robotic nodes, ensuring low-latency operation and enabling modular and scalable designs.