The world population is expected to grow to around 9 billion by 2050. The growing need for foods with high protein levels makes aquaculture one of the fastest-growing food industries in the world. Some challenges of fishing production are related to obsolete aquaculture techniques, overexploitation of marine species, and lack of water quality control. This research systematically analyzes aquaculture technologies, such as sensors, artificial intelligence (AI), and image processing. Through the systematic PRISMA process, 753 investigations published from 2012 to 2023 were analyzed based on a search in Scopus and Web of Science. It revealed a significant 70.5% increase in the number of articles published compared to the previous year, indicating a growing interest in this field. The results indicate that current aquaculture technologies are water monitoring sensors, AI methodologies such as K-means, and contour segmentation for computer vision. Also, it is reported that K means technologies offer an efficiency from 95% to 98%. These methods allow decisions based on data patterns and aquaculture insights. Improving aquaculture methodologies will allow adequate management of economic and environmental resources to promote fishing and satisfy nutritional needs.

Surface electromyography (sEMG) is widely used to evaluate neuromuscular activity; however, objective and reproducible methodologies for assessing signal quality in embedded acquisition systems remain limited. This study presents a multichannel embedded sEMG acquisition platform based on an ADS1298 analog front-end (Texas Instruments, Dallas, TX, USA) and an STM32H743ZIT6 microcontroller (STMicroelectronics, Geneva, Switzerland), together with a signal-quality evaluation methodology guided by signal-integrity principles derived from IEC 60601-2-40. sEMG signals were acquired at 2 kHz from 10 healthy participants during standardized submaximal isometric and controlled isotonic contractions using consistent electrode placement and acquisition procedures. Signal quality was quantified using complementary temporal and spectral metrics, including signal-to-noise ratio (SNR), power-line interference ratio (PLI), baseline drift, root mean square stability, median frequency, spectral entropy, skewness, and kurtosis. Signal conditioning reduced the baseline drift from 0.32 ± 0.23 to 0.004 ± 0.003 and reduced PLI from 0.097 ± 0.135 to 0.010 ± 0.003 while preserving contraction-dependent temporal and spectral characteristics. In addition, envelope-based Spearman correlation analysis revealed contraction-dependent intermuscular coordination patterns between the long and short heads of the biceps brachii under controlled acquisition conditions. These findings demonstrate that combining standardized acquisition protocols, objective signal quality metrics, and interpretable correlation-based analysis provides a reproducible engineering framework for evaluating embedded sEMG systems and establishes a foundation for future machine learning approaches based on larger and well-characterized physiological datasets.