Bibliographic citations
Rey-Sanchez, H., Velasquez, R. (2024). Desarrollo de un sistema electrónico de procesamiento de señales electromiografías que permita monitorear y extraer características de la actividad muscular en tiempo real orientado a aumentar la calidad y eficiencia en los diagnósticos fisioterapéuticos y sesiones de rehabilitación de los deportistas evaluados en el área de Fisioterapia de la UPC [Trabajo de suficiencia profesional, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/674719
Rey-Sanchez, H., Velasquez, R. Desarrollo de un sistema electrónico de procesamiento de señales electromiografías que permita monitorear y extraer características de la actividad muscular en tiempo real orientado a aumentar la calidad y eficiencia en los diagnósticos fisioterapéuticos y sesiones de rehabilitación de los deportistas evaluados en el área de Fisioterapia de la UPC [Trabajo de suficiencia profesional]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2024. http://hdl.handle.net/10757/674719
@misc{renati/419509,
title = "Desarrollo de un sistema electrónico de procesamiento de señales electromiografías que permita monitorear y extraer características de la actividad muscular en tiempo real orientado a aumentar la calidad y eficiencia en los diagnósticos fisioterapéuticos y sesiones de rehabilitación de los deportistas evaluados en el área de Fisioterapia de la UPC",
author = "Velasquez Fermin, Rai Santos",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2024"
}
This document proposes a precise, portable, ergonomic and low-cost electronic system for acquiring, conditioning, and processing electromyographic (EMG) signals. The system aims to monitor muscle activity and extract objective features of each muscle contraction in real time. This information can be used to complement the decisions made by physiotherapists in the diagnosis, rehabilitation, and monitoring of athletes. The system captures the muscle signal using surface electrodes, it is then conditioned by a differential amplifier and active analog filters. The signal is then digitized and rectified. Then the envelope is obtained in real time, and it is sectioned using an activity detection algorithm. Finally, it is processed with statistical and arithmetic algorithms in the time domain. This process allows us to obtain quantitative values such as the effort made, the duration of the contraction, the muscle activation time, and the loss of force of each contraction. These values are visualized and compared using graphs in a mobile application and stored per session in a database server for later analysis. The signals from the proposed system were compared with the PowerLab system, validating its accuracy. A correlation coefficient of 0.6729 was obtained between the acquired and conditioned signals, 0.7160 between the rectified signals, and 0.9883 between the envelope signals, which demonstrates the equivalence between both systems. In conclusion, the proposed system offers an efficient, portable and economical solution for the measurement, processing, and feature extraction of muscle activity. This information can be used to complement the decision-making of physiotherapists in the diagnosis and rehabilitation of athletes, providing more accurate, efficient, and lower-risk results.
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