# Conception of a System-on-Chip (SoC) Platform to Enable EMG-Guided Robotic Neurorehabilitation
## 5 Conclusions  
The proposed architecture based on a System-on-Chip (SoC), embodies key edge computing principles for EMG signal processing. By performing local data processing at the acquisition site, such as high-speed analog-to-digital conversion, advanced filtering, and initial signal processing, it significantly reduces latency and enables responsiveness in real-time. This approach minimizes raw data transmission, optimizing bandwidth while enhancing signal-to-noise ratio (SNR) and overall data integrity, which are critical for reliable robotic neurorehabilitation applications. The integration of the ADS1298R chip with an FPGA-based architecture, specifically the Zynq-7000 platform, addresses the limitations of microcontroller-based systems, such as their inability to handle high acquisition rates and to support the full bandwidth of EMG signals (0.01 Hz to 10 kHz). This approach not only reduces dependency on external processing but also improves modularity and scalability, making it suitable for advanced applications like integration with exoskeletons and other rehabilitation technologies. While the developed system demonstrates significant advancements, it still faces challenges, such as the computational load associated with higher channel counts and sampling frequencies. Nevertheless, the FPGA architecture offers the flexibility needed to overcome these limitations in future iterations. The next development stages will include integrating real-time processing capabilities directly into the SoC and continuously optimizing system resources. Finally, the proposed system represents not only a technical breakthrough but also establishes a solid foundation for applications in robotic rehabilitation and adaptive control of medical devices, reaffirming its relevance in the field of neurorehabilitation. This architecture is highly adaptable and can be utilized in various neurorehabilitation applications, including the control of assistive robots for rehabilitation. Its modularity and scalability make it an ideal candidate for integration with exoskeletons and other advanced rehabilitation technologies, ensuring its continued relevance and applicability in the evolving field of medical device control and neurorehabilitation. Conceptualization, R.N., V.M.N., P.R.F. and S.M. ; methodology, V.M.N., P.R.F., S.B. and R.N. ; software, V.M.N. and R.N. ; validation, V.M.N., R.N. and S.B. ; formal analysis, V.M.N. and R.N. ; investigation, P.R.F., V.M.N. and R.N. ; resources, [PERSON]; data curation, V.M.N. and R.N. ; writing--original draft preparation, V.M.N., P.R.F., S.M. and R.N. ; writing--review and editing, P.R.F., S.M. and R.N. ; visualization, R.N. and V.M.N. ; supervision, S.B. ; project administration, A.J.d.-A. and S.B. ; funding acquisition, A.J.d.-A. and S.B. All authors have read and agreed to the published version of the manuscript. This work is part of the I+D+I project PID2021-123657 OB-C31, funded by MCIN/AEI/ 10.13039/501100011033/, in part by the ExoSen-SoC Project (ref. T2023/00004/03-M2998), funded by Rey Juan Carlos University and \"FEDER Una manera de hacer Europa\". The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Rey Juan Carlos University (protocol code \begin{table}
\begin{tabular}{c c c c} \hline
**Component** & **Slice LUTs** & **Slice Registers** & **Block RAM Tile** \\ \hline Global System & 2798 (15.9\%) & 4189 (11.9\%) & 3 (5\%) \\ ADS Ctrl SPI & 130 (0.74\%) & 273 (0.78\%) & 0.5 (0.83\%) \\ ADS Mux SPI & 54 (0.31\%) & 170 (0.48\%) & 0 (0\%) \\ DMA & 1013 (5.76\%) & 1520 (4.32\%) & 2.5 (4.17\%) \\ AXI Quad SPI & 376 (2.14\%) & 575 (1.63\%) & 0 (0\%) \\ Additional blocks & 778 (4.42\%) & 921 (2.62\%) & 0 (0\%) \\ \hline \end{tabular}
\end{table} 
Table 2: Hardware resource consumption for TE0727 (Z7-10) SoC-FPGA. 040320241492024, approval date: 4 March 2024).