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Tesi etd-09242017-173258


Thesis type
Tesi di laurea magistrale
Author
PILLA, ALESSANDRO
URN
etd-09242017-173258
Title
Design and validation of novel methods for assessment of upper-limb spasticity through a powered elbow exoskeleton
Struttura
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
INGEGNERIA BIOMEDICA
Supervisors
relatore Prof. Vitiello, Nicola
correlatore Dott.ssa Crea, Simona
Parole chiave
  • wearable robotics
  • rehabilitation robotics
  • elbow exoskeleton
  • spasticity assessment
Data inizio appello
13/10/2017;
Consultabilità
Parziale
Data di rilascio
13/10/2020
Riassunto analitico
Stroke is a neurological deficit attributed to an acute focal injury of the central nervous system (CNS) by a vascular cause, including cerebral infarction, intracerebral hemorrhage, and subarachnoid hemorrhage. Every year, in Italy, 200.000 people suffer a stroke, 80% of which represent new episodes, while the remaining 20% are relapses. Common symptoms observed in post-stroke patients include severe sensory and motor hemiparesis of the contralesional side of the body, loss of dexterity, and spasticity. Although many clinical scales are available to therapists to evaluate the rank of spasticity, their main limitation is the uncertainty and approximation due to the qualitative evaluation of the patient’s conditions.
The purpose of this work is to propose a novel method for the assessment of elbow spasticity based on kinetic and kinematic data recorded by means of a powered elbow exoskeleton, namely NEEM (NEUROExos Elbow Module, available for use at the BioRobotics Institute, Scuola Superiore Sant’Anna), and surface electromyography data. A Graphical User Interface (GUI) was developed in LabVIEW© environment, which allows the therapist to customize the assessment program and perform passive and active flexion-extension movements with the exoskeleton at different speeds, while monitoring and recording the patient’s and robot parameters (i.e. electromyography signals, joint angle and joint torque).
The assessment program was tested on 8 healthy subjects and a small group of patients. A MATLAB© code was compiled for the offline analysis of acquired data, in order to extract different parameters related to the patient’s muscles activation and degree of spasticity (i.e. Stretch Reflex Onset, EMG Burst Duration, joint stiffness and others). Results suggest that the proposed method can be potentially used in clinical applications to provide therapists with complementary, quantitative data on the joint spasticity. Further investigations will validate the robot measurements with the outcomes of clinical scales.
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