Banca de QUALIFICAÇÃO: ANA PAULA MENDONÇA FERNANDES

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
STUDENT : ANA PAULA MENDONÇA FERNANDES
DATE: 06/04/2024
TIME: 09:30
LOCAL: presencial
TITLE:

Development of a device for kinematic analysis and functional physical performance of the upper limb in people with Amyotrophic Lateral Sclerosis

KEY WORDS:

Amyotrophic Lateral Sclerosis; Surface Electromyography; Accelerometer; Data Analysis; Functionality

PAGES: 81
BIG AREA: Ciências da Saúde
AREA: Fisioterapia e Terapia Ocupacional
SUMMARY:

Amyotrophic Lateral Sclerosis (ALS) is a progressive and neurodegenerative disease characterized by gradual motor limitations, individually affecting each person and interfering with their quality of life. Complications in the upper limbs exert considerable influence on daily activities and functional autonomy. The utilization of data from surface electromyography (EMGs) and accelerometry (ACC) plays a crucial role in analyzing and characterizing functional capacity, resulting in more precise and reliable assessments. These data not only assist in diagnostic processes and exercise prescription but also enable precise monitoring of disease progression and provide data for managing advanced assistive technologies, such as orthoses with artificial intelligence systems and wheelchair control systems, leveraging the precise categorization of information obtained through machine learning algorithms (ML). The objective of this work is to develop an accessible EMG system capable of capturing and processing analog data, which will subsequently be integrated into a device capable of recording muscular and kinematic signals, through accelerometry sensors, to assess upper limb functional performance in people affected by ALS. After capture, the data will undergo a system aimed at filtering and extracting statistical properties and signal characteristics, seeking the categorization of muscle fatigue indices through an ML algorithm. After system development, tests will be conducted with both healthy individuals and those with ALS, collecting movements resembling daily activities, to verify the effectiveness of the device in terms of its operational properties and the analysis of captured signals. For data correlation purposes, muscle analysis will be conducted simultaneously using the device produced in this work and the TRIGNO™ Wireless System from Delsys Inc., an electromyograph characterized by its high performance. Additionally, the results will be correlated with fatigue and functionality scales validated for people with ALS. The Wilcoxon-Mann-Whitney statistical test will be used to compare data obtained from ACC and EMGs in each task, both in GS and GE groups, and with both sensors. Additionally, paired sample tests, such as the Friedman test, were conducted to analyze possible statistical differences between task types within the GS and GE groups. Thus, this work proposes an innovative and accessible methodology, integrating monitoring and data analysis technologies, such as EMGs and ACC, along with machine learning algorithms, to diagnose, monitor, and assist in the development of individualized therapeutic objectives, significantly contributing to improving the quality of life of people with ALS, highlighting the importance of an interdisciplinary approach to addressing the challenges presented by the disease

COMMITTEE MEMBERS:
Presidente - 2179208 - ANA RAQUEL RODRIGUES LINDQUIST
Interna - 1081828 - CATARINA DE OLIVEIRA SOUSA
Externo à Instituição - ERNANO ARRAIS JUNIOR - UFERSA