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Application of machine learning techniques for the myoelectric control of prosthetics
Thesis abroad
keywords FOREARM MUSCLES, HIGH-DENSITY SURFACE EMG, MOTION ANALYSIS
Reference persons TAIAN MARTINS
External reference persons Prof James Fitzgerald
Research Groups Laboratory for Engineering of the Neuromuscular System (LISiN)
Thesis type EXPERIMENTAL
Description The proposed topic is part of a major project, which ultimate goal is the development of a novel Machine Learning (ML) - based strategy to control articulated hand prostheses.
High-density surface EMG is used as the control variable. The project takes an holistic approach moving from the development of the brain computer interface, through the acquisition of data on subjects, to the development of the ML architecture, and to a testing phase on amputees.
The research is currently conducted at the University of Oxford, while data acquisition is conducted at Politecnico di Torino. Out of the two activities to be conducted as a Master Thesis project, outlined below, the first study is to be pursued at the moment.
Study 1:
The MSc final project will fit in the research framework just outlined. The student will be given access to the EMG and kinematic data set acquired in Politecnico di Torino and will develop a ML-based architecture to translate the former to the latter.
Study 2:
The MSc final project will fit in the research framework just outlined. The student will take part in the design and execution phase of the data acquisition process. After a satisfying amount of data of sufficient quality has been acquired, the student will assess the classification/regression performance of a ML algorithm.
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Required skills Linear algebra
Advanced Matlab skills
High, English proficiency
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Notes Students willing to undertake this proposal are requested to write to the supervisor stating:
1- the interest to pursue this proposal
2- elements motivating the decision to consider pursuing this thesis (5-10 lines of text)
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Deadline 19/05/2023
PROPONI LA TUA CANDIDATURA