Desarrollo de sistema para medir el retardo electromecánico (EMD) del bíceps en rehabilitación.
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Date
2025
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Publisher
Universidad de Concepción
Abstract
El estudio del retardo electromecánico (EMD) en rehabilitación y medicina deportiva es esencial para mejorar la efectividad de la terapia y el rendimiento atlético. El EMD representa una demora en la respuesta de un sistema electromecánico a una señal eléctrica de entrada, afectando diversos aspectos como la inercia mecánica y el procesamiento de señales. Para abordar este fenómeno, se emplean técnicas de filtrado, modelado matemático, simulación, métodos experimentales y control adaptativo. En la actualidad, optar por estas tecnologías supone un amplio gasto tanto en lo económico como en lo computacional, debido a que las técnicas para medir este fenómeno requieren el uso de equipos específicos y de alto costo, necesarios para obtener las señales de la mejor calidad posible. Además, para un estudio más completo, es necesario medir otros parámetros que requieren equipos aún más especializados. El uso de herramientas avanzadas y tecnologías inalámbricas ha ampliado las posibilidades de aplicación en entornos clínicos y deportivos. Para este proyecto, se utilizaron sensores económicos pero funcionales para medir señales de electromiografía (EMG) y señales de mecanomiografía (MMG) para la captura de la actividad muscular. Además, se empleó el software MATLAB para el procesamiento de señales fisiológicas y un microcontrolador Arduino para la adquisición de datos en tiempo real. Para la medición de las señales de EMD, se tomaron muestras a sujetos jóvenes deportistas, lo que permitió obtener datos representativos de individuos en óptimas condiciones físicas. Varios factores, como la edad, el nivel de condición física, el historial de lesiones, el tono muscular, la composición corporal y el nivel de fatiga, influyen en la medición y adquisición de señales de EMD. Es crucial considerar estas variables para obtener mediciones precisas y representativas del EMD.
The study of electromyographic delay (EMD) in rehabilitation and sports medicine is essential for improving the effectiveness of therapy and athletic performance. EMD represents a delay in the response of an electromechanical system to an input electrical signal, affecting various aspects such as mechanical inertia and signal processing. To address this phenomenon, techniques such as filtering, mathematical modeling, simulation, experimental methods, and adaptive control are employed. Currently, opting for these technologies entails significant economic and computational costs, as the techniques to measure this phenomenon require the use of specific, high-cost equipment necessary to obtain the highest quality signals. Additionally, for a more comprehensive study, it is necessary to measure other parameters that require even more specialized equipment. The use of advanced tolos and Wireless technologies has expanded the possibilities of application in clinical and sports settings.For this project, economical yet functional sensors were used to measure electromyographic (EMG) signals and mechanomyographic (MMG) signals for capturing muscle activity. Furthermore, MATLAB software was employed for processing physiological signals, and an Arduino microcontroller was used for real time data acquisition. For the measurement of EMD signals, simples were taken from Young athletes, which allowed for the collection of representative data from individuals in optimal physical condition. Several factors, such as age, fitness level, injury history, muscle tone, body composition, and fatigue level, influence the measurement and acquisition of EMD signals. It is crucial to consider these variables to obtain accurate and representative EMD measurements.
The study of electromyographic delay (EMD) in rehabilitation and sports medicine is essential for improving the effectiveness of therapy and athletic performance. EMD represents a delay in the response of an electromechanical system to an input electrical signal, affecting various aspects such as mechanical inertia and signal processing. To address this phenomenon, techniques such as filtering, mathematical modeling, simulation, experimental methods, and adaptive control are employed. Currently, opting for these technologies entails significant economic and computational costs, as the techniques to measure this phenomenon require the use of specific, high-cost equipment necessary to obtain the highest quality signals. Additionally, for a more comprehensive study, it is necessary to measure other parameters that require even more specialized equipment. The use of advanced tolos and Wireless technologies has expanded the possibilities of application in clinical and sports settings.For this project, economical yet functional sensors were used to measure electromyographic (EMG) signals and mechanomyographic (MMG) signals for capturing muscle activity. Furthermore, MATLAB software was employed for processing physiological signals, and an Arduino microcontroller was used for real time data acquisition. For the measurement of EMD signals, simples were taken from Young athletes, which allowed for the collection of representative data from individuals in optimal physical condition. Several factors, such as age, fitness level, injury history, muscle tone, body composition, and fatigue level, influence the measurement and acquisition of EMD signals. It is crucial to consider these variables to obtain accurate and representative EMD measurements.
Description
Tesis presentada para optar al título de Ingeniero/a Civil Biomédico.
Keywords
Medicina deportiva, Rehabilitación, Fisioterapia, Electromiografía