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mgr inż. Mateusz Troka
Employment
- PhD Student at Szkoła Doktorska
- 2019 - present PhD Student at Mechanical Engineering Department of Structural Mechanics
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total: 6
Catalog Publications
Year 2024
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Self-organising maps in the analysis of strains of human abdominal wall to identify areas of similar mechanical behaviour.
PublicationThe study refers to the application of a type of artificial neural network called the Self-Organising Map (SOM) for the identification of areas of the human abdominal wall that behave in a similar mechanical way. The research is based on data acquired during in vivo tests using the digital image correlation technique (DIC). The mechanical behaviour of the human abdominal wall is analysed during changing intra-abdominal pressure....
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Unsupervised Learning for Biomechanical Data Using Self-organising Maps, an Approach for Temporomandibular Joint Analysis
PublicationWe proposed to apply a specific machine learning technique called Self-Organising Maps (SOM) to identify similarities in the performance of muscles around human temporomandibular joint (TMJ). The performance was assessed by measuring muscle activation with the use of surface electromyography (sEMG). SOM algorithm used in the study was able to find clusters of data in sEMG test results. The SOM analysis was based on processed sEMG...
Year 2023
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Full-field in vivo experimental study of the strains of a breathing human abdominal wall with intra-abdominal pressure variation
PublicationThe presented study aims to assess the mechanical behaviour of the anterior abdominal wall based on an in vivo experiment on humans. Full-field measurement of abdominal wall displacement during changes of intra-abdominal pressure is performed using a digital image correlation (DIC) system. Continuous measurement in time enables the observation of changes in the strain field during breathing. The understanding of the mechanical...
Year 2022
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Towards classification of patients based on surface EMG data of temporomandibular joint muscles using self-organising maps
Publication -
Towards classification of patients based on surface EMG data of temporomandibular joint muscles using self-organising maps
PublicationThe study considers the need for an effective method of classification of patients with a temporomandibular joint disorder (TMD). The self-organising map method (SOM) was applied to group patients and used together with the cross-correlation approach to interpret the processed (rectified and smoothed by using root mean square (RMS) algorithm) surface electromyography signal (sEMG) obtained from testing the muscles (two temporal...
Year 2021
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Self-Organising map neural network in the analysis of electromyography data of muscles acting at temporomandibular joint.
PublicationThe temporomandibular joint (TMJ) is the joint that via muscle action and jaw motion allows for necessary physiological performances such as mastication. Whereas mandible translates and rotates [1]. Estimation of activity of muscles acting at the TMJ provides a knowledge of activation pattern solely of a specific patient that an electromyography (EMG) examination was carried out [2]. In this work, a Self-Organising Maps (SOMs)...
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