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In this work, we present CogBeacon, a multi-modal dataset designed to target the effects of cognitive fatigue in human performance. The dataset consists of 76 sessions collected from 19 male and female users performing different versions of a cognitive task inspired by the principles of the Wisconsin Card Sorting Test (WCST), a popular cognitive test in experimental and clinical psychology designed to assess cognitive flexibility, reasoning, and specific aspects of cognitive functioning. During each session, we record and fully annotate user EEG functionality, facial keypoints, real-time self-reports on cognitive fatigue, as well as detailed information of the performance metrics achieved during the cognitive task (success rate, response time, number of errors, etc.). Along with the dataset we provide free access to the CogBeacon data-collection software to provide a standardized mechanism to the community for collecting and annotating physiological and behavioral data for cognitive fatigue analysis. Our goal is to provide other researchers with the tools to expand or modify the functionalities of the CogBeacon data-collection framework in a hardware-independent way. As a proof of concept we show some preliminary machine learning-based experiments on cognitive fatigue detection using the EEG information and the subjective user reports as ground truth. Our experiments highlight the meaningfulness of the current dataset, and encourage our efforts towards expanding the CogBeacon platform. To our knowledge, this is the first multi-modal dataset specifically designed to assess cognitive fatigue and the only free software available to allow experiment reproducibility for multi-modal cognitive fatigue analysis.
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Cognitive Fatigue Assessment Using Physiological Sensors during Human-Robot Interaction for Activities of Daily Living
A multimodal dataset is presented for the cognitive fatigue assessment of physiological minimally invasive sensory data of Electrocardiography (ECG) and Electrodermal Activity (EDA) and self-reporting scores of cognitive fatigue during HRI. Data were collected from 16 non-STEM participants, up to three visits each, during which the subjects interacted with a robot to prepare a meal and get ready for work. For some of the visits, a well-established cognitive test was used to induce cognitive fatigue. The developed cognitive fatigue assessment framework filtered noise from the raw signals, extracted relevant features, and applied machine learning regression algorithms, such as Support Vector Regression (SVR), Gradient Boosting Machine (GBM), and Random Forest Regressor (RFR) for estimating the Cognitive Fatigue (CF) level.
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- Award ID(s):
- 2226165
- PAR ID:
- 10615213
- Publisher / Repository:
- IEEE DataPort
- Date Published:
- Format(s):
- Medium: X
- Right(s):
- Creative Commons Attribution 4.0 International
- Sponsoring Org:
- National Science Foundation
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