| KEYWORD |
Detection of muscle activity after Anterior Cruciate Ligament (ACL) reconstruction: analysis of landing tasks
keywords ACL, LANDING, MACHINE LEARNING, MUSCLE ACTIVITY, NEUROENGINEERING, SEMG, SIGNAL PROCESSING
Reference persons VALENTINA AGOSTINI, MARCO GHISLIERI
Research Groups Biolab: Ingegneria Biomedica
Description This thesis aims to investigate and analyze muscle activity patterns in athletes who have undergone Anterior Cruciate Ligament (ACL) reconstruction during different landing tasks. The primary focus is on employing novel sEMG processing techniques to extract muscle activation intervals and on extracting relevant features to capture distinctive patterns associated with muscle activity during landing tasks. The thesis seeks to enhance our understanding of muscle activity patterns in athletes after ACL reconstruction, offering a foundation for improved rehabilitation strategies and diagnostic tools. The findings may aid in optimizing recovery protocols, reducing the risk of re-injury, and promoting the long-term well-being of athletes undergoing ACL reconstruction.
For more information, please send an email to marco.ghislieri@polito.it
Required skills Basic knowledge of MATLAB (data management and visualization), signal processing, basic knowledge of ML model training, and Neuroengineering exam passed with a score equal to or higher than 27/30.
Notes The thesis assignment will occur after the proposal deadline.
Deadline 15/02/2024
PROPONI LA TUA CANDIDATURA