Pregled bibliografske jedinice broj: 1023065
MMOD-COG: A Database for Multimodal Cognitive Load Classification
MMOD-COG: A Database for Multimodal Cognitive Load Classification // Proceedings of the 11th International Symposium on Image and Signal Processing and Analysis / Lončarić, Sven ; Bregović, Robert ; Carli, Marco ; Subašić, Marko (ur.).
Zagreb: University of Zagreb, Croatia, 2019. str. 15-20 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1023065 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
MMOD-COG: A Database for Multimodal Cognitive Load Classification
Autori
Mijić, Igor ; Šarlija, Marko ; Petrinović, Davor
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 11th International Symposium on Image and Signal Processing and Analysis
/ Lončarić, Sven ; Bregović, Robert ; Carli, Marco ; Subašić, Marko - Zagreb : University of Zagreb, Croatia, 2019, 15-20
ISBN
978-1-7281-3140-5
Skup
11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019)
Mjesto i datum
Dubrovnik, Hrvatska, 23.09.2019. - 25.09.2019
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
multimodal classification of cognitive load ; speech responses ; physiological responses
Sažetak
This paper presents a dataset for multimodal classification of cognitive load recorded on a sample of students. The cognitive load was induced by way of performing basic arithmetic tasks, while the multimodal aspect of the dataset comes in the form of both speech and physiological responses to those tasks. The goal of the dataset was two-fold: firstly to provide an alternative to existing cognitive load focused datasets, usually based around Stroop tasks or working memory tasks ; and secondly to implement the cognitive load tasks in a way that would make the responses appropriate for both speech and physiological response analysis, ultimately making it multimodal. The paper also presents preliminary classification benchmarks, in which SVM classifiers were trained and evaluated solely on either speech or physiological signals and on combinations of the two. The multimodal nature of the classifiers may provide improvements on results on this inherently challenging machine learning problem because it provides more data about both the intra- participant and inter-participant differences in how cognitive load manifests itself in affective responses.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo, Kognitivna znanost (prirodne, tehničke, biomedicina i zdravstvo, društvene i humanističke znanosti)
POVEZANOST RADA
Projekti:
KK.01.1.1.01.0009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (EK )
0036054
HRZZ-IP-2014-09-2625 - Iznad Nyquistove granice (BeyondLimit) (Seršić, Damir, HRZZ ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb