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Tesi etd-04032020-172144


Tipo di tesi
Tesi di laurea magistrale
Autore
LIA, MARGHERITA MARIA
URN
etd-04032020-172144
Titolo
Study and development of algorithms for motor patterns and anomalies recognition in Parkinson's Disease
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
INGEGNERIA BIOMEDICA
Relatori
relatore Prof. Cavallo, Filippo
relatore Dott.ssa Rovini, Erika
Parole chiave
  • algorithms
  • motor patterns
  • parkinson
Data inizio appello
24/04/2020
Consultabilità
Non consultabile
Data di rilascio
24/04/2090
Riassunto
The diagnosis of Parkinson's disease through neurological evaluation considers the hesitations during the execution of the motor tasks determinant to assign a judgment of severity. The experience of neurologists in daily clinical practice has shown that hesitation can also be found in healthy subjects. However, there is no universally recognized convention among neurologists to identify and quantify hesitations. This thesis aims to introduce both ways of trying to quantify hesitations and broaden the horizon on why to look for these parameters and enrich the collection of objective characteristics of motor patterns on both healthy and patient with Parkinson’s disease at all stages of pathology.
A motor exercise agreed with the neurologist and a traditional exercise for evaluation were administered to the cohort of recruits. The participants wore a high precision wearable system during the execution. The exercises were recorded on video and the signals of the wearable device were extracted. An algorithm has been developed to identify movement anomalies and to objectively characterize the motor pattern. Hesitations were detected for each test and placed in the time duration of each exercise. Finally, the outputs of the algorithm were compared with the video visual inspection. The study showed that evaluation with the naked eye of the neurologist must be supported by a precise instrument to make the analysis of the movement objective and automatic. Further researches are needed to gather normative values of traditional and new parameters and build a decision support system for the neurologist in the motor assessment of PD.
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