In this thesis, we focused on the problem of measuring and visualizing arm movement, which is crucial in various fields such as rehabilitation, sports performance
analysis, and ergonomic assessments. Traditional methods, including optical motion capture systems, are often expensive, complex, and limited to controlled
environments, making their broader use challenging. Inertial Measurement Units
(IMUs) offer an alternative solution due to their accessibility, portability, ease of
use, and low cost. The system integrates three IMU sensors placed on the forearm, upper arm, and torso, which send data to a computer via a USB receiver. The data is then
processed using the UltraSimple and QuaternionIntegration algorithms for precise
real-time motion tracking. These algorithms combine data from accelerometers,
gyroscopes, and magnetometers to accurately calculate orientation and movement.
For measuring orientation, we use quaternions, which provide a stable and
accurate representation of rotations without the issues encountered with Euler
angles. By combining data from the three sensors, the system captures comprehensive motion data, allowing real-time visualization of arm movements using
Unity software. For data validation and precise analysis, I use Matlab, where the
data is displayed in 3D graphs. The main findings of the research show that the combination of UltraSimple and QuaternionIntegration algorithms is effective for tracking complex rotational arm movements. Data validation performed in Matlab confirmed the accuracy
and reliability of the system, enabling its use in various applications such as rehabilitation, sports performance analysis, and ergonomic assessments. Results have shown that the system allows for precise and robust tracking of arm movement,
which is essential for providing reliable data in various applications.
Despite all the advantages of IMUs, challenges remain in ensuring data accuracy and reliability, especially during prolonged use. Errors can accumulate,
and sensitivity to external factors such as magnetic fields and vibrations can negatively impact measurement accuracy. Therefore, I focused on the proper use
of algorithms for data capture and visualization and the correct calibration of
sensors to reduce errors and improve system accuracy.
Gathered results contribute to the improvement of the current state in the field
of measuring and visualizing arm movements using IMU sensors. The system we
used is affordable, portable, and easy to use, allowing for its broader practical
application. Further research is possible to improve the accuracy and reliability
of the system, especially through the use of advanced machine learning methods
for error detection and compensation, and the integration of additional sensors
for more comprehensive motion tracking.
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