%0 Conference Proceedings
%T Machine Learning for Cognitive Load Classification – A Case Study on Contact-Free Approach
%+ Mälardalen University (MDH)
%A Ahmed, Mobyen, Uddin
%A Begum, Shahina
%A Gestlöf, Rikard
%A Rahman, Hamidur
%A Sörman, Johannes
%Z Part 1: Classification
%< avec comité de lecture
%@ 978-3-030-49160-4
%( Artificial Intelligence Applications and Innovations
%B 16th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Neos Marmaras, Greece
%Y Ilias Maglogiannis
%Y Lazaros Iliadis
%Y Elias Pimenidis
%I Springer International Publishing
%3 IFIP Advances in Information and Communication Technology
%V AICT-583
%N Part I
%P 31-42
%8 2020-06-05
%D 2020
%R 10.1007/978-3-030-49161-1_3
%K Cognitive Load (CL)
%K Contact-free approach
%K k-Nearest-Neighbor (k-NN)
%K Support Vector Machines (SVM)
%K Machine Learning (ML)
%Z Computer Science [cs]Conference papers
%X The most common ways of measuring Cognitive Load (CL) is using physiological sensor signals e.g., Electroencephalography (EEG), or Electrocardiogram (ECG). However, these signals are problematic in situations e.g., in dynamic moving environments where the user cannot relax with all the sensors attached to the body and it provides significant noises in the signals. This paper presents a case study using a contact-free approach for CL classification based on Heart Rate Variability (HRV) collected from ECG signal. Here, a contact-free approach i.e., a camera-based system is compared with a contact-based approach i.e., Shimmer GSR+ system in detecting CL. To classify CL, two different Machine Learning (ML) algorithms, mainly, Support Vector Machine (SVM) and k-Nearest-Neighbor (k-NN) have been applied. Based on the gathered Inter-Beat-Interval (IBI) values from both the systems, 13 different HRV features were extracted in a controlled study to determine three levels of CL i.e., S0: low CL, S1: normal CL and S2: high CL. To get the best classification accuracy with the ML algorithms, different optimizations such as kernel functions were chosen with different feature matrices both for binary and combined class classifications. According to the results, the highest average classification accuracy was achieved as 84% on the binary classification i.e. S0 vs S2 using k-NN. The highest F1 score was achieved 88% using SVM for the combined class considering S0 vs (S1 and S2) for contact-free approach i.e. the camera system. Thus, all the ML algorithms achieved a higher classification accuracy while considering the contact-free approach than contact-based approach.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-04050591/document
%2 https://inria.hal.science/hal-04050591/file/497040_1_En_3_Chapter.pdf
%L hal-04050591
%U https://inria.hal.science/hal-04050591
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-583