{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:53:17Z","timestamp":1759333997018,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SECIHTI of Mexico"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Algorithms"],"abstract":"<jats:p>This paper presents a novel hardware architecture for implementing real-time EMG feature extraction and dimensionality reduction in resource-constrained FPGA environments. The proposed co-processing architecture integrates four time-domain feature extractors (MAV, WL, SSC, ZC) with a specialized PCA matrix multiplication unit within a unified processing pipeline, demonstrating significant improvements in power efficiency and processing latency compared to traditional software-based approaches. Multiple matrix multiplication architectures are evaluated to optimize FPGA resource utilization while maintaining deterministic real-time performance using a Zed evaluation board as the development platform. This implementation achieves efficient dimensionality reduction with minimal hardware resources, making it suitable for embedded prosthetic applications. The functionality of this system is validated using a custom EMG database from previous studies. The results demonstrate a 7.3\u00d7 speed improvement and 3.1\u00d7 energy efficiency gain compared to ARM Cortex-A9 software implementation, validating the architectural approach for battery-powered prosthetic control applications.<\/jats:p>","DOI":"10.3390\/a18100617","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T09:12:23Z","timestamp":1759223543000},"page":"617","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hardware\u2013Software Co-Design Architecture for Real-Time EMG Feature Processing in FPGA-Based Prosthetic Systems"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2453-371X","authenticated-orcid":false,"given":"Carlos Gabriel","family":"Mireles-Preciado","sequence":"first","affiliation":[{"name":"Facultad de Inform\u00e1tica, Universidad Aut\u00f3noma de Quer\u00e9taro, Quer\u00e9taro 76230, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2230-6751","authenticated-orcid":false,"given":"Diana Carolina","family":"Toledo-P\u00e9rez","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro, Quer\u00e9taro 76010, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7501-5272","authenticated-orcid":false,"given":"Roberto Augusto","family":"G\u00f3mez-Loenzo","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro, Quer\u00e9taro 76010, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2838-4854","authenticated-orcid":false,"given":"Marcos","family":"Aviles","sequence":"additional","affiliation":[{"name":"Facultad de Inform\u00e1tica, Universidad Aut\u00f3noma de Quer\u00e9taro, Quer\u00e9taro 76230, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8598-5600","authenticated-orcid":false,"given":"Juvenal","family":"Rodr\u00edguez-Res\u00e9ndiz","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad Aut\u00f3noma de Quer\u00e9taro, Quer\u00e9taro 76010, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,30]]},"reference":[{"key":"ref_1","first-page":"1497","article-title":"Efficient implementation of artificial neural networks on FPGAs using high-level synthesis and parallelism","volume":"11","author":"Namboothiripad","year":"2024","journal-title":"Int. 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