prikaz prve stranice dokumenta Raspoznavanje stresa na temelju analize fizioloških značajki metodama dubokog učenja
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undergraduate thesis
Raspoznavanje stresa na temelju analize fizioloških značajki metodama dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. urn:nbn:hr:168:958526
Nikolić, Ljubica
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Nikolić, Lj. (2024). Raspoznavanje stresa na temelju analize fizioloških značajki metodama dubokog učenja (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:958526

Nikolić, Ljubica. "Raspoznavanje stresa na temelju analize fizioloških značajki metodama dubokog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:958526

Nikolić, Ljubica. "Raspoznavanje stresa na temelju analize fizioloških značajki metodama dubokog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2024. https://urn.nsk.hr/urn:nbn:hr:168:958526

Nikolić, Lj. (2024). 'Raspoznavanje stresa na temelju analize fizioloških značajki metodama dubokog učenja', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 29 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:958526

Nikolić Lj. Raspoznavanje stresa na temelju analize fizioloških značajki metodama dubokog učenja [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2024 [cited 2024 December 29] Available at: https://urn.nsk.hr/urn:nbn:hr:168:958526

Lj. Nikolić, "Raspoznavanje stresa na temelju analize fizioloških značajki metodama dubokog učenja", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2024. Available at: https://urn.nsk.hr/urn:nbn:hr:168:958526

Metadata
TitleRaspoznavanje stresa na temelju analize fizioloških značajki metodama dubokog učenja
Title (english)Stress recognition based on the analysis of physiological features using deep learning methods
AuthorLjubica Nikolić
MentorSiniša Popović (mentor)
Committee memberSiniša Popović (predsjednik povjerenstva)
Committee memberHrvoje Nuić (član povjerenstva)
Committee memberMate Gambiraža (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2024-07-03, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovaj rad se bavi prepoznavanjem stresa temeljenog na analizi fizioloških značajki koristeći metode dubokog učenja. Korištena je javno dostupna baza podataka WESAD (Wearable Stress and Affect Detection), koja sadrži multivarijatne podatke prikupljene putem uređaja RespiBAN. To se odnosi na skup signala koji obuhvaćaju elektrokardiogram (ECG), elektrodermalnu aktivnost (EDA), elektromiogram (EMG), temperaturu (TEMP), akceleraciju u 3 smjera (ACC; 3 signala) i respiraciju (RESP). Podaci su prethodno obrađeni i normalizirani kako bi mogli poslužiti kao ulaz u modele auto-enkodera. Razvijeni su modeli koji koriste sirove podatke i modeli koji koriste valićno transformirane podatke. Rezultati su pokazali da model koji koristi sirove podatke ima bolju točnost od 79.3 % te F1 rezultat od 78.5 % u prepoznavanju stresa u usporedbi s modelom koji koristi valićnu transformaciju kojem je točnost 71.5 % te F1 rezultat 73.3 %.
Abstract (english)
This paper deals with stress recognition based on the analysis of physiological features using deep learning methods. The publicly available WESAD (Wearable Stress and Affect Detection) database was used, containing multivariate data collected via RespiBAN devices. This pertains to a set of signals encompassing electrocardiogram (ECG), electrodermal activity (EDA), electromyogram (EMG), temperature (TEMP), acceleration in 3 directions (ACC; 3 signals), and respiration (RESP). The data was preprocessed and normalized to serve as input to autoencoder models. Models utilizing raw data and models employing wavelet-transformed data were developed. The results indicated that the model using raw data achieved higher accuracy of 79.3 % and F1 score of 78.5 % in stress recognition compared to the model using wavelet transformation, which achieved an accuracy of 71.5 % and F1 score of 73.3 %.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:958526
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
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File originBorn digital
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Created on2024-10-12 21:40:59