MEDICIÓN DE ILUMINANCIA EN UNA FUENTE DE FOTOTERAPIA

Este documento trata acerca de las mediciones de luz realizadas a un dispositivo de fototerapia diseñado en el Instituto Tecnológico de Mérida (ITM). La finalidad del dispositivo es para ser usado como fuente de fototerapia para tratar la ictericia neonatal. Las características del diseño fueron obtenidas de documentos científicos, normas y hojas de datos relacionados con el tema de la Fototerapia en el recién nacido.Las principales características para el diseño fueron la longitud de onda de la luz emitida, el área iluminada y la Iluminancia (lux) que se refiere a la cantidad de flujo luminoso que incide sobre una superficie por unidad de área.Se presentan brevemente las etapas de diseño del prototipo, así como el procedimiento de medición realizado para obtener las características de iluminancia del dispositivo

Energy Efficient Framework for a AIoT Cardiac Arrhythmia Detection System Wearable during Sport

The growing market of wearables is expanding into different areas of application such as devices designed to improve and monitor sport activities. This in turn is pushing research on low-cost, very low-power wearable systems with increased analysis capabilities. This paper proposes integrated energy-aware techniques and a convolutional neural network (CNN) for a cardiac arrhythmia detection system that can be worn during sport training sessions. The dynamic power management strategy (DPMS) is programmed into an ultra-low-power microcontroller, and in combination with a photovoltaic (PV) energy harvesting (EH) circuit, achieves a battery-life extension towards a self-powered operation. The CNN-based analysis filters, scales the image, and using a bicubic technique, interpolates the measurements to subsequently classify the electrocardiogram (ECG) signal into normal and abnormal patterns. Experimental results show that the EH-DPMS achieves an extension in the battery charge for a total of 14.34% more energy available, which represents 12 consecutive workouts of 45 min without the need to manually recharge it. Furthermore, an arrhythmia detection precision of 98.6% is achieved among the experimental sessions using 55,222 images for training the system with the MIT-BIH, QT, and long-term ST databases, and 1320 implemented on a wearable system. Therefore, the proposed wearable system can be used to monitor an athlete’s condition, reducing the risk of abnormal heart conditions during sports activities

Energy Efficient Framework for a AIoT Cardiac Arrhythmia Detection System Wearable during Sport

The growing market of wearables is expanding into different areas of application such as devices designed to improve and monitor sport activities. This in turn is pushing research on low-cost, very low-power wearable systems with increased analysis capabilities. This paper proposes integrated energy-aware techniques and a convolutional neural network (CNN) for a cardiac arrhythmia detection system that can be worn during sport training sessions. The dynamic power management strategy (DPMS) is programmed into an ultra-low-power microcontroller, and in combination with a photovoltaic (PV) energy harvesting (EH) circuit, achieves a battery-life extension towards a self-powered operation. The CNN-based analysis filters, scales the image, and using a bicubic technique, interpolates the measurements to subsequently classify the electrocardiogram (ECG) signal into normal and abnormal patterns. Experimental results show that the EH-DPMS achieves an extension in the battery charge for a total of 14.34% more energy available, which represents 12 consecutive workouts of 45 min without the need to manually recharge it. Furthermore, an arrhythmia detection precision of 98.6% is achieved among the experimental sessions using 55,222 images for training the system with the MIT-BIH, QT, and long-term ST databases, and 1320 implemented on a wearable system. Therefore, the proposed wearable system can be used to monitor an athlete’s condition, reducing the risk of abnormal heart conditions during sports activities