| KEYWORD |
Pose estimation: using artificial intelligence to assess knee joint angles
Thesis abroad
keywords DEEP LEARNING, SMARTPHONES
Reference persons TAIAN MARTINS
External reference persons Prof Antonio Dello Iacono
University of the West of Scotland
Research Groups Laboratory for Engineering of the Neuromuscular System (LISiN)
Thesis type EXPERIMENTAL APPLIED
Description Different approaches have been proposed and used for assessing joint angles. The possibility of doing so is of relevance in applied fields, from rehabilitation intervention to assessment of performance in sports. In this thesis we investigate whether innovative methods, based on the application of artificial intelligence to video data, for the estimation of human pose provide accurate estimates of joint angle.
Required skills Good understanding of human biomechanics
Linear algebra
Advanced Matlab and Python skills
High English proficiency
Background in resistance training is an asset
Notes Students willing to undertake this proposal are requested to contact the supervisor and submit:
1. A letter of interest for the proposed project
2. Elements motivating the decision to pursue this thesis (5-10 lines of text)
Deadline 20/12/2024
PROPONI LA TUA CANDIDATURA