"Monitoreo Inteligente para la Rehabilitación Pulmonar: Un Enfoque Innovador con PPG"

Autores/as

DOI:

https://doi.org/10.56913/teceo.6.12.151-169

Palabras clave:

Fotopletismografía, rehabilitación pulmonar, rehabilitación en casa, bioseñales, tecnología no invasiva, índice de perfusión periférica, pulsos por minuto, saturación de oxígeno

Resumen

Este artículo presenta el desarrollo de un sistema portátil que permite el monitoreo del proceso de respiración en una rehabilitación utilizando la técnica de fotopletismografía (PPG por sus siglas en inglés). El sistema incluye una tarjeta Xiao ESP32 C3, un sensor MAX30102 y una batería de polímero de litio, que se utilizan para capturar información de luz infrarroja y roja. La transmisión inalámbrica de estos datos se realiza a través de una interfaz gráfica disponible en Python y una aplicación móvil para Android. Además de permitir la visualización en tiempo real de la señal PPG, el sistema tiene un procedimiento de monitoreo a los cambios de la perfusión periférica durante ejercicios respiratorios, lo que hace posible la estimación de calcular la frecuencia cardíaca (BPM por sus siglas en inglés), la saturación de oxígeno (SpO2), y el Índice de Perfusión Periférica (PPI por sus siglas en inglés). Los algoritmos del software permiten un análisis en tiempo real, mostrando el comportamiento del PPI. Este desarrollo tiene como principal objetivo proporcionar una herramienta accesible y no invasiva para el monitoreo de la respiración, con un enfoque particular en el monitoreo de la rehabilitación. A pesar de la falta de estudios clínicos en este documento, el sistema, como tal, representa la plataforma para su futuro desarrollo clínico potencial y proporciona a los médicos una oportunidad para monitorear continuamente registros de los datos de bioseñales a distancia. En la actualidad, el sistema aún no ha sido validado para su fiabilidad y validez clínica, buscará obtener la aprobación en futuros estudios con pacientes que padecen enfermedades respiratorias crónicas.

Biografía del autor/a

Alonso Alejandro Jiménez-Garibay, Tecnológico Nacional de México en Celaya

This paper presents the development of a portable system for monitoring the breathing process during rehabilitation using the photoplethysmography (PPG) technique. The system includes a Xiao ESP32 C3 board, a MAX30102 sensor, and a lithium polymer battery, which are used to capture infrared and red-light information. Wireless transmission of these data is achieved through a graphical interface available in Python and a mobile application for Android. In addition to real-time visualization of the PPG signal, the system includes a monitoring procedure for changes in peripheral perfusion during respiratory exercises, enabling the estimation and calculation of heart rate (BPM), oxygen saturation (SpO2), and Peripheral Perfusion Index (PPI). The software algorithms allow for real-time analysis, displaying the behavior of the PPI. The main objective of this development is to provide an accessible and non-invasive tool for monitoring respiration, with a particular focus on rehabilitation. Despite the lack of clinical studies in this paper, the system represents a platform for potential future clinical development and offers clinicians the opportunity to continuously monitor biosignal data remotely. Currently, the system has not yet been validated for reliability and clinical validity, but it aims to gain approval in future studies with patients suffering from chronic respiratory diseases.

Coral Martínez-Nolasco, Tecnológico Nacional de México en Celaya

This paper presents the development of a portable system for monitoring the breathing process during rehabilitation using the photoplethysmography (PPG) technique. The system includes a Xiao ESP32 C3 board, a MAX30102 sensor, and a lithium polymer battery, which are used to capture infrared and red-light information. Wireless transmission of these data is achieved through a graphical interface available in Python and a mobile application for Android. In addition to real-time visualization of the PPG signal, the system includes a monitoring procedure for changes in peripheral perfusion during respiratory exercises, enabling the estimation and calculation of heart rate (BPM), oxygen saturation (SpO2), and Peripheral Perfusion Index (PPI). The software algorithms allow for real-time analysis, displaying the behavior of the PPI. The main objective of this development is to provide an accessible and non-invasive tool for monitoring respiration, with a particular focus on rehabilitation. Despite the lack of clinical studies in this paper, the system represents a platform for potential future clinical development and offers clinicians the opportunity to continuously monitor biosignal data remotely. Currently, the system has not yet been validated for reliability and clinical validity, but it aims to gain approval in future studies with patients suffering from chronic respiratory diseases.

Mauro Santoyo-Mora, Tecnológico Nacional de México en Celaya

This paper presents the development of a portable system for monitoring the breathing process during rehabilitation using the photoplethysmography (PPG) technique. The system includes a Xiao ESP32 C3 board, a MAX30102 sensor, and a lithium polymer battery, which are used to capture infrared and red-light information. Wireless transmission of these data is achieved through a graphical interface available in Python and a mobile application for Android. In addition to real-time visualization of the PPG signal, the system includes a monitoring procedure for changes in peripheral perfusion during respiratory exercises, enabling the estimation and calculation of heart rate (BPM), oxygen saturation (SpO2), and Peripheral Perfusion Index (PPI). The software algorithms allow for real-time analysis, displaying the behavior of the PPI. The main objective of this development is to provide an accessible and non-invasive tool for monitoring respiration, with a particular focus on rehabilitation. Despite the lack of clinical studies in this paper, the system represents a platform for potential future clinical development and offers clinicians the opportunity to continuously monitor biosignal data remotely. Currently, the system has not yet been validated for reliability and clinical validity, but it aims to gain approval in future studies with patients suffering from chronic respiratory diseases.

Adriana Guzmán-López, Tecnológico Nacional de México en Celaya

This paper presents the development of a portable system for monitoring the breathing process during rehabilitation using the photoplethysmography (PPG) technique. The system includes a Xiao ESP32 C3 board, a MAX30102 sensor, and a lithium polymer battery, which are used to capture infrared and red-light information. Wireless transmission of these data is achieved through a graphical interface available in Python and a mobile application for Android. In addition to real-time visualization of the PPG signal, the system includes a monitoring procedure for changes in peripheral perfusion during respiratory exercises, enabling the estimation and calculation of heart rate (BPM), oxygen saturation (SpO2), and Peripheral Perfusion Index (PPI). The software algorithms allow for real-time analysis, displaying the behavior of the PPI. The main objective of this development is to provide an accessible and non-invasive tool for monitoring respiration, with a particular focus on rehabilitation. Despite the lack of clinical studies in this paper, the system represents a platform for potential future clinical development and offers clinicians the opportunity to continuously monitor biosignal data remotely. Currently, the system has not yet been validated for reliability and clinical validity, but it aims to gain approval in future studies with patients suffering from chronic respiratory diseases.

Citas

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Zhong, Y., Jatav, A., Afrin, K., Shivaram, T., & Bukkapatnam, S. T. S. (2023). Enhanced SpO2 estimation using explainable machine learning and neck photoplethysmography. Artificial Intelligence in Medicine, 145, 102685. https://doi.org/10.1016/j.artmed.2023.102685

Archivos adicionales

Publicado

2024-12-31

Cómo citar

Palacios-Campos, J. G. U., Jiménez-Garibay, A. A., Martínez-Nolasco, C., Santoyo-Mora, M., & Guzmán-López, A. (2024). "Monitoreo Inteligente para la Rehabilitación Pulmonar: Un Enfoque Innovador con PPG". Tecnología, Ciencia Y Estudios Organizacionales, 6(12), 151–169. https://doi.org/10.56913/teceo.6.12.151-169

Número

Vol. 6 Núm. 12 (2024): TeCEO 12

Sección

Artículos

Licencia

Derechos de autor 2024 Tecnología, Ciencia y Estudios Organizacionales

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.

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