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A graph-based approach to study muscle coordination during walking in patients affected by Parkinsonís disease



Gruppi di ricerca Biolab: Ingegneria Biomedica

Descrizione Even the simplest of human movements involves the coordinated activity of a multitude of muscles to generate the desired movement task. To achieve multi-muscle coordination, numerous studies have suggested a robust and low-dimensional control of muscles through the combination of motor modules (or muscle synergies). Muscle synergies are functional units that generate a motor output by imposing a specific activation pattern on a group of muscles. Their identification relies on the combination of surface electromyographic (sEMG) recordings over numerous muscles through factorization algorithms that extract consistent patterns of activity across muscles. It has been hypothesized that each synergy would be associated with a single neural command, which would in turn decrease the computational load of motor control. However, one of the main drawbacks of the present theory is that, depending on individual characteristics, type and number of muscles acquired, sEMG pre-processing techniques applied, and factorization algorithm implemented, the number and the composition of the extracted muscle synergies may change. Alternative techniques, such as graph-based approaches, are being explored to extract muscle synergies and assess motor coordination, overcoming the limitation of the standard approaches. According to this theory, muscle coordination can be modelled as networks/graphs in which each node represents the activity of a single muscle and where the nodes are positioned close to each other or further apart, depending on the level of correlated activities of the corresponding sEMG signals. This thesis aims at assessing the applicability of a graph-based approach, applied to sEMG signals acquired during walking, to study muscle coordination. To evaluate the accuracy and validity of the proposed approach, the obtained results will be compared to the muscle synergies extracted from the same sEMG data. The proposed approach will be also validated considering sEMG signals acquired from subjects with different musculoskeletal or neurological disorders (e.g., Parkinsonís disease patients) to assess its applicability also in different pathological conditions.

Conoscenze richieste Basic knowledge of MATLAB (data management and visualization), Signal processing, and Neuroengineering exam passed with a score equal to or higher than 27/30.

Note The proposal will be described during a meeting on Tuesday, July 18, 2023, at 11:00. VC Link: https://didattica.polito.it/pls/portal30/sviluppo.bbb_corsi.waitRoom?id=3614&p_tipo=DOCENTE

Scadenza validita proposta 25/07/2023      PROPONI LA TUA CANDIDATURA

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