Arnulfo Alanis

Population aging has increased the need for continuous, non-invasive, and context-aware monitoring systems capable of supporting autonomy, safety, and early intervention in daily living environments. Multimodal biometric sensing offers an important technical basis for this purpose, as it combines physiological, motion-related, and environmental signals to provide a more complete view of older adults’ functional and health-related conditions. However, many existing solutions remain fragmented, device-dependent, and insufficiently connected to core instrumentation requirements, including signal quality, sensor calibration, temporal synchronization, latency, energy consumption, interoperability, reliability, and data privacy. This article proposes an Edge-AI instrumentation framework for multimodal biometric sensing in active aging environments, supported by a structured analysis of recent literature on wearable, ambient, and context-aware sensing systems. The framework integrates wearable, ambient, and context-aware sensors with local processing capabilities to support signal acquisition, preprocessing, quality control, feature extraction, anomaly detection, and decision support close to the data source. By placing Edge AI within the instrumentation pipeline, the proposed framework identifies design requirements that may help reduce response time, limit unnecessary transmission of sensitive biometric data, and improve feasibility in home-based and assisted-living contexts. These expected benefits, however, require empirical testing through future prototype implementation and real-world evaluation. The article also defines a validation-oriented perspective for sensor-based active aging systems, covering technical, operational, and human-centered dimensions such as measurement accuracy, signal robustness, usability, privacy preservation, interoperability, reproducibility, energy efficiency, and system scalability. The proposed framework is intended to support the design, comparison, and validation of more reliable, interpretable, and reproducible sensor-based monitoring systems, while offering a structured basis for prototype development and future real-world evaluation in active aging environments.

Aquaponic systems show complex multivariate dynamics in water quality parameters, with dissolved oxygen (DO) being a key indicator of biological stability. This study presents a dynamic multivariate predictive framework for short-term dissolved oxygen forecasting utilizing IoT data gathered from various heterogeneous aquaponic ponds. The issue is redefined as a regression task to forecast future DO values within a brief time-frame (~5 min), enabling early warning functionalities instead of utilizing a rule-based classification method. To ensure structural robustness across systems, we applied intra-pond percentile trimming and normalization procedures to mitigate the differences in scale between ponds. Using a Leave-One-Pond-Out (LOPO) validation scheme, we tested model performance and cross-system generalization. An MLP feedforward neural network with lagged temporal variables had an average RMSE of 0.83 on a normalized scale. Regime-based error analysis showed that the RMSE increased from 0.80 on stable conditions to 1.43 under high-volatile regimes. A comparative LSTM model did not produce substantial performance enhancements. Sensitivity analysis revealed lagged impacts of pH and turbidity on subsequent DO dynamics, indicating the need for operational measures such as aeration modification and suspended solids management.

En este trabajo se presenta una revisión de literatura sobre el uso de la Inteligencia Artificial Generativa (IAG) en la Educación Superior. Se revisaron artículos de las bases de datos y sitios siguientes: Scopus, Web of Science, IEEE Xplore y Google Scholar. Para el análisis se seleccionaron 40 documentos, en el periodo de 2018 al 2024. Se consideraron los siguientes criterios: artículos, acceso abierto, texto completo, educación superior. El objetivo del análisis es identificar el impacto que tiene el uso de la IAG como apoyo en el aprendizaje dentro de las universidades. A partir de la revisión bibliográfica y según lo planteado por algunos autores, se revisarán las posibilidades de como se ha integrado el uso de la IAG dentro de las universidades.

In Mexico, the community of people with hearing disabilities faces significant communication barriers due to the limited availability of interpreters and the lack of supportive technological tools. This paper presents the development of a sign language system using artificial intelligence (SLSAI), a mobile application that uses artificial intelligence (AI) for the recognition of Mexican sign language (MSL). Using machine learning techniques and image processing, SLSAI translates signs from the alphabet in real-time, facilitating interaction between deaf and hearing individuals. Additionally, the tool features a module where students can learn sign language through interactive games, creating a fun and engaging learning environment. The development methods, obtained results, and social implications of the tool are discussed.

The paper examines the risks and benefits of digital technology affecting child development. A mobile application was created to help parents monitor and control their children’s use of electronic devices. The methodology included interviews with a child psychologist, documentary research, the creation of the application, and questionnaires for parents before and after its implementation. The results indicated that after using the application, children experienced a 40% reduction in screen time and a 20% improvement in academic performance. Additionally, a positive change in children’s attitudes toward technology was observed, resulting in greater interest in participating in physical activities and interactive games. The study concludes that mobile applications effectively promoted a more balanced use of digital technology in childhood, highlighting the role of parents and the need for ongoing education on how to use technology responsibly.