Herramienta computacional basada en el seguimiento de trayectoria en ejercicios de rehabilitación de rodilla usando modelos NME y sensores inerciales
This degree project proposes the development of a computational tool to support knee rehabilitation through a system for monitoring and analyzing flexion and extension movements. The tool uses inertial sensors (IMUs) as an alternative to traditional motion capture systems, allowing flexible monitori...
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| Format: | Tesis/Trabajo de grado - Monografía - Pregrado |
| Language: | Español |
| Published: |
Universidad Antonio Nariño
2025
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| Online Access: | https://repositorio.uan.edu.co/handle/123456789/12426 |
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| Summary: | This degree project proposes the development of a computational tool to support knee rehabilitation through a system for monitoring and analyzing flexion and extension movements. The
tool uses inertial sensors (IMUs) as an alternative to traditional motion capture systems, allowing
flexible monitoring in different environments and activities. The process includes simulating movements in OpenSim software to define reference trajectories in four positions (standing, sitting, and
lying on the back and stomach), integrating musculoskeletal models that approximately represent
the anatomy and movement of the knee.
A platform was developed in MATLAB that processes the data in quaternions obtained from
the IMU sensors placed in the pelvis, femur and tibia, converting them to Euler angles to calculate
the knee flexion angle. Motion data, extracted into CSV files, allows you to generate trajectory
graphs and compare patient performance against reference trajectories. For the analysis, a trajectory tracking algorithm was implemented, capable of calculating the range of motion (RoM), the
percentage of error and the number of correct repetitions, allowing detailed control of the patient’s
progress. In addition, an application was designed to store and view patient data, making it easier
for health professionals to analyze the progress in each therapy session.
The system was validated in three people with limited, moderate and advanced mobility under
a specific protocol, allowing its precision and robustness to be evaluated in the capture and processing of knee rehabilitation movements. The results obtained demonstrated the effectiveness of
the system in estimating key biomechanical parameters, confirming its potential for application in
rehabilitation environments. The conclusions highlight the viability of this tool as a support in the
monitoring and analysis of the recovery process |
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