About: Machine Learning for Cognitive Load Classification – A Case Study on Contact-Free Approach

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Machine Learning for Cognitive Load Classification – A Case Study on Contact-Free Approach Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Machine Learning for Cognitive Load Classification – A Case Study on Contact-Free Approach
Creator	Begum, Shahina Gestlöf, Rikard Rahman, Hamidur Sörman, Johannes Uddin, Mobyen
Source	PMC
abstract	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 F(1) 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.
has issue date	2020-05-06(xsd:dateTime)
bibo:doi	10.1007/978-3-030-49161-1_3
has license	no-cc
sha1sum (hex)	d691f95073f3358113e89d0027e64853e8ab49f0
schema:url	https://doi.org/10.1007/978-3-030-49161-1_3
resource representing a document's title	Machine Learning for Cognitive Load Classification – A Case Study on Contact-Free Approach
has PubMed Central identifier	PMC7256406
schema:publication	Artificial Intelligence Applications and Innovations
resource representing a document's body	covid:d691f95073f3358113e89d0027e64853e8ab49f0#body_text
is schema:about of	named entity 'environments' named entity 'combined' named entity 'Machine Learning' named entity 'Case Study' named entity 'PRESENTS' named entity 'ELECTROCARDIOGRAM' named entity 'WHERE' named entity 'INTER-' named entity 'SVM' named entity 'PROVIDES' named entity 'SENSOR' named entity 'MEASURING' named entity 'FREE' named entity 'PROBLEMATIC' named entity 'DETERMINE' named entity 'NEAREST' named entity 'BEST' named entity 'RELAX' named entity 'CLASSIFY' named entity 'ALGORITHMS' named entity 'CHOSEN' named entity 'BINARY CLASSIFICATION' named entity 'CONTACT' named entity 'NEIGHBOR' named entity 'k-NN' named entity 'average' named entity 'Here' named entity 'higher' named entity 'However' named entity 'values' named entity 'body' named entity 'detecting' named entity 'kernel' named entity 'noises' named entity 'sensors' named entity 'determine' named entity 'Classification' named entity 'k-NN' named entity 'ECG' named entity 'Support Vector Machine' named entity 'Heart Rate Variability' named entity 'binary classification' named entity 'EEG' named entity 'ECG' named entity 'controlled study' named entity 'Machine Learning' named entity 'Case Study' named entity 'feature selection' named entity 'time domain' named entity 'fatigue' named entity 'sigmoid function' named entity 'visual attention' named entity 'k-NN' named entity 'human error' named entity 'EEG' named entity 'controlled experiment' named entity 'Data Processing' named entity '20%' named entity 'video recording' named entity 'test subject' named entity 'supervised learning' named entity 'F 1' named entity 'SVM' named entity 'heart rate' named entity 'test subjects' named entity 'test subjects' named entity 'test subjects' named entity 'test subjects' named entity 'test subjects' named entity 'frequency-domain' named entity 'video game' named entity 'SVM' named entity 'SVM' named entity 'test subject' named entity 'test subjects' named entity 'physiological' named entity 'SVM' named entity 'GSR' named entity 'Data Set'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software