Electromyography Signals in Embedded Systems: A Review of Processing and Classification Techniques
This article provides an overview of the implementation of electromyography (EMG) signal classification algorithms in various embedded system architectures. They address the specifications used for implementation in different devices, such as the number of movements and the type of classification me...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/3/166 |
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| author | José Félix Castruita-López Marcos Aviles Diana C. Toledo-Pérez Idalberto Macías-Socarrás Juvenal Rodríguez-Reséndiz |
| author_facet | José Félix Castruita-López Marcos Aviles Diana C. Toledo-Pérez Idalberto Macías-Socarrás Juvenal Rodríguez-Reséndiz |
| author_sort | José Félix Castruita-López |
| collection | DOAJ |
| description | This article provides an overview of the implementation of electromyography (EMG) signal classification algorithms in various embedded system architectures. They address the specifications used for implementation in different devices, such as the number of movements and the type of classification method. Architectures analyzed include microcontrollers, DSP, FPGA, SoC, and neuromorphic computers/chips in terms of precision, processing time, energy consumption, and cost. This analysis highlights the capabilities of each technology for real-time wearable applications such as smart prosthetics and gesture control devices, as well as the importance of local inference in artificial intelligence models to minimize execution times and resource consumption. The results show that the choice of device depends on the required system specifications, the robustness of the model, the number of movements to be classified, and the limits of knowledge concerning design and budget. This work provides a reference for selecting technologies for developing embedded biomedical solutions based on EMG. |
| format | Article |
| id | doaj-art-8eaa7fcc3b3f408d8e5da67b8a97b477 |
| institution | DOAJ |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-8eaa7fcc3b3f408d8e5da67b8a97b4772025-08-20T02:42:45ZengMDPI AGBiomimetics2313-76732025-03-0110316610.3390/biomimetics10030166Electromyography Signals in Embedded Systems: A Review of Processing and Classification TechniquesJosé Félix Castruita-López0Marcos Aviles1Diana C. Toledo-Pérez2Idalberto Macías-Socarrás3Juvenal Rodríguez-Reséndiz4Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro 76240, MexicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro 76240, MexicoFacultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro 76240, MexicoFacultad de Ciencias Agrarias, Universidad Estatal Península de Santa Elena (UPSE), Santa Elena 240204, EcuadorFacultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro 76240, MexicoThis article provides an overview of the implementation of electromyography (EMG) signal classification algorithms in various embedded system architectures. They address the specifications used for implementation in different devices, such as the number of movements and the type of classification method. Architectures analyzed include microcontrollers, DSP, FPGA, SoC, and neuromorphic computers/chips in terms of precision, processing time, energy consumption, and cost. This analysis highlights the capabilities of each technology for real-time wearable applications such as smart prosthetics and gesture control devices, as well as the importance of local inference in artificial intelligence models to minimize execution times and resource consumption. The results show that the choice of device depends on the required system specifications, the robustness of the model, the number of movements to be classified, and the limits of knowledge concerning design and budget. This work provides a reference for selecting technologies for developing embedded biomedical solutions based on EMG.https://www.mdpi.com/2313-7673/10/3/166embedded systemsEMGartificial intelligenceclassification algorithmsFPGASoC |
| spellingShingle | José Félix Castruita-López Marcos Aviles Diana C. Toledo-Pérez Idalberto Macías-Socarrás Juvenal Rodríguez-Reséndiz Electromyography Signals in Embedded Systems: A Review of Processing and Classification Techniques Biomimetics embedded systems EMG artificial intelligence classification algorithms FPGA SoC |
| title | Electromyography Signals in Embedded Systems: A Review of Processing and Classification Techniques |
| title_full | Electromyography Signals in Embedded Systems: A Review of Processing and Classification Techniques |
| title_fullStr | Electromyography Signals in Embedded Systems: A Review of Processing and Classification Techniques |
| title_full_unstemmed | Electromyography Signals in Embedded Systems: A Review of Processing and Classification Techniques |
| title_short | Electromyography Signals in Embedded Systems: A Review of Processing and Classification Techniques |
| title_sort | electromyography signals in embedded systems a review of processing and classification techniques |
| topic | embedded systems EMG artificial intelligence classification algorithms FPGA SoC |
| url | https://www.mdpi.com/2313-7673/10/3/166 |
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