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master's thesis
Raspoznavanje fizičke aktivnosti zasnovano na strojnom učenju
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:006090
Jurčić, Krunoslav
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Jurčić, K. (2021). Raspoznavanje fizičke aktivnosti zasnovano na strojnom učenju (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:006090

Jurčić, Krunoslav. "Raspoznavanje fizičke aktivnosti zasnovano na strojnom učenju." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:006090

Jurčić, Krunoslav. "Raspoznavanje fizičke aktivnosti zasnovano na strojnom učenju." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:006090

Jurčić, K. (2021). 'Raspoznavanje fizičke aktivnosti zasnovano na strojnom učenju', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 06 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:006090

Jurčić K. Raspoznavanje fizičke aktivnosti zasnovano na strojnom učenju [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 November 06] Available at: https://urn.nsk.hr/urn:nbn:hr:168:006090

K. Jurčić, "Raspoznavanje fizičke aktivnosti zasnovano na strojnom učenju", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:006090

Metadata
TitleRaspoznavanje fizičke aktivnosti zasnovano na strojnom učenju
Title (english)Physical Activity Recognition Based on Machine Learning
AuthorKrunoslav Jurčić
MentorRatko Magjarević (mentor)
Committee memberRatko Magjarević (predsjednik povjerenstva)
Committee memberTomislav Pribanić (član povjerenstva)
Committee memberDinko Oletić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-15, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Motivacija za razvoj algoritma za raspoznavanje fizičke aktivnosti uključuje praćenje dnevne aktivnosti korisnika u stvarnom vremenu, te moguću dijagnostiku neželjenih i neočekivanih pokreta. Algoritam za raspoznavanje razvijen je koristeći dva skupa podataka koji sadrže signale dobivene iz akcelerometra, magnetometra i žiroskopa. Prvi otvoreni skup podataka, dostupan svim korisnicima, sadrži signale koji opisuju pet fizičkih aktivnosti. Drugi skup opisuje sveukupno 16 aktivnosti, koje su nakon grupiranja svrstane u 12 klasa. Od toga 9 aktivnosti pripada svakodnevnim aktivnostima, a tri pripadaju neočekivanim pokretima, točnije padovima. Najveću točnost klasifikacije oba skupa ostvarile su metode slučajne šume (96.45%, 87.10%) i stroja potpornih vektora (95.12%, 81.72%). Osim raspoznavanja svakodnevnih aktivnosti, algoritmi su izvrsne rezultate postigli i pri detekciji i razlikovanju padova (97.83% i 86.96%), što upućuje na njihovu široku upotrebu u biomedicinskom inženjerstvu.
Abstract (english)
The motivation for the development of physical activity recognition algorithm includes keeping track of user's activities in real time, and possible diagnostics of unwanted and unexpected movement. Activity recognition algorithm was trained on two datasets containing signals from an accelerometer, a magnetometer and a gyroscope. The first open access dataset contains signals describing five physical activities. The other dataset describes a total of 16 physical activities, which were clustered into 12 classes. A total of nine classes describe activities of daily living, while the other three belong to unexpected movements, more precisely falls. The best results of classification on both datasets were achieved by Random forest (RF) algorithm (96.45%, 87.10%) and Support vector machine (SVM) algorithm (95.12%, 81.72%). Aside from recognition of daily activities, the algorithms also achieved excellent results when detecting and distinguishing fall types (97.83% and 86.96%), which suggests of their wide use in biomedical engineering.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:006090
Study programmeTitle: Information and Communication Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije (magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije)
Type of resourceText
File originBorn digital
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Created on2023-01-13 12:33:43