Measuring Cognitive Workload Using Multimodal Sensors
- URL: http://arxiv.org/abs/2205.04235v1
- Date: Thu, 5 May 2022 23:18:00 GMT
- Title: Measuring Cognitive Workload Using Multimodal Sensors
- Authors: Niraj Hirachan, Anita Mathews, Julio Romero, Raul Fernandez Rojas
- Abstract summary: This study aims to identify a set of indicators to estimate cognitive workload using a multimodal sensing approach and machine learning.
A set of three cognitive tests were conducted to induce cognitive workload in twelve participants at two levels of task difficulty (Easy and Hard)
- Score: 1.8582645184234494
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This study aims to identify a set of indicators to estimate cognitive
workload using a multimodal sensing approach and machine learning. A set of
three cognitive tests were conducted to induce cognitive workload in twelve
participants at two levels of task difficulty (Easy and Hard). Four sensors
were used to measure the participants' physiological change, including,
Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and
blood oxygen saturation (SpO2). To understand the perceived cognitive workload,
NASA-TLX was used after each test and analysed using Chi-Square test. Three
well-know classifiers (LDA, SVM, and DT) were trained and tested independently
using the physiological data. The statistical analysis showed that
participants' perceived cognitive workload was significantly different
(p<0.001) between the tests, which demonstrated the validity of the
experimental conditions to induce different cognitive levels. Classification
results showed that a fusion of ECG and EDA presented good discriminating power
(acc=0.74) for cognitive workload detection. This study provides preliminary
results in the identification of a possible set of indicators of cognitive
workload. Future work needs to be carried out to validate the indicators using
more realistic scenarios and with a larger population.
Related papers
- Auto Detecting Cognitive Events Using Machine Learning on Pupillary Data [0.0]
Pupil size is a valuable indicator of cognitive workload, reflecting changes in attention and arousal governed by the autonomic nervous system.
This study explores the potential of using machine learning to automatically detect cognitive events experienced using individuals.
arXiv Detail & Related papers (2024-10-18T04:54:46Z) - Wearable Device-Based Real-Time Monitoring of Physiological Signals: Evaluating Cognitive Load Across Different Tasks [6.673424334358673]
This study employs cutting-edge wearable monitoring technology to conduct cognitive load assessment on electroencephalogram (EEG) data of secondary vocational students.
The research delves into their application value in assessing cognitive load among secondary vocational students and their utility across various tasks.
arXiv Detail & Related papers (2024-06-11T10:48:26Z) - Characterizing Information Seeking Processes with Multiple Physiological Signals [12.771920957950334]
This study examines informational search with four stages: the realization of Information Need (IN), Query Formulation (QF), Query Submission (QS), and Relevance Judgment (RJ)
We analyze the physiological signals across these stages and report outcomes of pairwise non-parametric repeated-measure statistical tests.
Our findings offer valuable insights into user behavior and emotional responses in information seeking processes.
arXiv Detail & Related papers (2024-05-01T05:15:00Z) - Analyzing Participants' Engagement during Online Meetings Using Unsupervised Remote Photoplethysmography with Behavioral Features [50.82725748981231]
Engagement measurement finds application in healthcare, education, services.
Use of physiological and behavioral features is viable, but impracticality of traditional physiological measurement arises due to the need for contact sensors.
We demonstrate the feasibility of the unsupervised photoplethysmography (rmography) as an alternative for contact sensors.
arXiv Detail & Related papers (2024-04-05T20:39:16Z) - EEG-based Cognitive Load Classification using Feature Masked
Autoencoding and Emotion Transfer Learning [13.404503606887715]
We present a new solution for the classification of cognitive load using electroencephalogram (EEG)
We pre-train our model using self-supervised masked autoencoding on emotion-related EEG datasets.
The results of our experiments show that our proposed approach achieves strong results and outperforms conventional single-stage fully supervised learning.
arXiv Detail & Related papers (2023-08-01T02:59:19Z) - Evaluating the structure of cognitive tasks with transfer learning [67.22168759751541]
This study investigates the transferability of deep learning representations between different EEG decoding tasks.
We conduct extensive experiments using state-of-the-art decoding models on two recently released EEG datasets.
arXiv Detail & Related papers (2023-07-28T14:51:09Z) - A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable Sensors [56.554277096170246]
We present an empirical study that evaluates and contrasts four commonly employed annotation methods in user studies focused on in-the-wild data collection.
For both the user-driven, in situ annotations, where participants annotate their activities during the actual recording process, and the recall methods, where participants retrospectively annotate their data at the end of each day, the participants had the flexibility to select their own set of activity classes and corresponding labels.
arXiv Detail & Related papers (2023-05-15T16:02:56Z) - Using EEG Signals to Assess Workload during Memory Retrieval in a
Real-world Scenario [3.9763527484363292]
This study investigated the associations between memory workload and EEG during participants' typical office tasks.
We used EEG band power, mutual information, and coherence as features to train machine learning models to classify high versus low memory workload states.
arXiv Detail & Related papers (2023-05-14T02:01:54Z) - Dataset Bias in Human Activity Recognition [57.91018542715725]
This contribution statistically curates the training data to assess to what degree the physical characteristics of humans influence HAR performance.
We evaluate the performance of a state-of-the-art convolutional neural network on two HAR datasets that vary in the sensors, activities, and recording for time-series HAR.
arXiv Detail & Related papers (2023-01-19T12:33:50Z) - Uncovering the structure of clinical EEG signals with self-supervised
learning [64.4754948595556]
Supervised learning paradigms are often limited by the amount of labeled data that is available.
This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG)
By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
arXiv Detail & Related papers (2020-07-31T14:34:47Z) - Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using
Deep Multiple-Instance Learning [59.74684475991192]
Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old.
PD symptoms include tremor, rigidity and braykinesia.
We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
arXiv Detail & Related papers (2020-05-06T09:02:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.