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MAUS: A Dataset for Mental Workload Assessmenton N-back Task Using Wearable Sensor

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Win-Ken Beh, Yi-Hsuan Wu, An-Yeu (Andy) Wu

Signal Processing · Submitted 3 Nov 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The result of data analysis show the potential uses of the recorded modalities and the feasibility of the MW elicitation protocol.

how it is stated · hedged possibility 0.51 read the record →

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arXiv ID2111.02561 (version 1)
Pith IDPSYGSC2Q, Pith's permanent record of this paper and its review
CitationAn-Yeu (Andy) Wu, Win-Ken Beh, Yi-Hsuan Wu. "MAUS: A Dataset for Mental Workload Assessmenton N-back Task Using Wearable Sensor." arXiv:2111.02561 (2021). https://pith.science/paper/PSYGSC2Q
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,503 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.21 · human participants 0.98 · controlled comparison 0.17 · randomized assignment 0.02 · states quantitative result 0.98 · compares with prior results 0.67 · mentions code or data availability 0.99.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

This paper describes an open-access database focusing on the study of mental workload (MW) assessment system for wearable devices. A wristband photoplethysmogram (PPG) was provided as a representative of wearable devices. In addition, a clinical device that can record Electrocardiography (ECG) , galvanic skin response (GSR) and, fingertip PPG was included in the database as a reference. The MW was induced by performing the N-back task with 22 subjects. The participants were asked to answer the Pittsburgh Sleep Quality Index (PSQI) questionnaire at the beginning of the experiment and the NASA Task Load Index (NASA-TLX) questionnaire after each N-back task. The result of data analysis show the potential uses of the recorded modalities and the feasibility of the MW elicitation protocol. Finally the MAUS dataset is now available for academic use (The MAUS dataset is available at IEEE Dataport: https://ieee-dataport.org/open-access/maus-dataset-mental-workload-assessment-n-back-task-using-wearable-sensor). Besides, we also presents a reproducible baseline system as a preliminary benchmark (The code of the baseline system on MAUS dataset is available on Github: https://github.com/rickwu11/MAUS\_dataset\_baseline\_system), which testing accuracy are 71.6 %, 66.7 %, and 59.9 % in ECG, fingertip PPG, wristband PPG, respectively.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This paper describes an open-access database focusing on the study of mental workload (MW) assessment system for wearable devices. contribution 0.99 0.63 0.06 asserted 0.99
s1 A wristband photoplethysmogram (PPG) was provided as a representative of wearable devices. method 0.95 0.69 0.03 asserted 1.00
s2 In addition, a clinical device that can record Electrocardiography (ECG) , galvanic skin response (GSR) and, fingertip PPG was included in the database as a reference. method 0.93 0.75 0.03 asserted 1.00
s3 The MW was induced by performing the N-back task with 22 subjects. method 0.99 0.91 0.03 asserted 1.00
s4 The participants were asked to answer the Pittsburgh Sleep Quality Index (PSQI) questionnaire at the beginning of the experiment and the NASA Task Load Index (NASA-TLX) questionnaire after each N-back task. method 1.00 0.89 0.03 asserted 0.98
s5 The result of data analysis show the potential uses of the recorded modalities and the feasibility of the MW elicitation protocol. interpretation 0.66 0.93 0.73 hedged possibility 0.51
s6 Finally the MAUS dataset is now available for academic use (The MAUS dataset is available at IEEE Dataport: https://ieee-dataport.org/open-access/maus-dataset-mental-workload-assessment-n-back-task-using-wearable-sensor). contribution 0.95 0.64 0.06 asserted 1.00
s7 Besides, we also presents a reproducible baseline system as a preliminary benchmark (The code of the baseline system on MAUS dataset is available on Github: https://github.com/rickwu11/MAUS\_dataset\_baseline\_system), which testing accuracy are 71.6 %, 66.7 %, and 59.9 % in ECG, fingertip PPG, wristband PPG, respectively. contribution 0.69 0.94 0.09 asserted 1.00

What kind of paper the classifier reports this is: dataset or benchmark 1.00 · evidence from observation or experiment 0.98.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

This paper describes an open-access database focusing on the study of mental workload (MW) assessment system for wearable devices. A wristband photoplethysmogram (PPG) was provided as a representative of wearable devices. In addition, a clinical device that can record Electrocardiography (ECG) , galvanic skin response (GSR) and, fingertip PPG was included in the database as a reference. The MW was induced by performing the N-back task with 22 subjects. The participants were asked to answer the Pittsburgh Sleep Quality Index (PSQI) questionnaire at the beginning of the experiment and the NASA Task Load Index (NASA-TLX) questionnaire after each N-back task. The result of data analysis show the potential uses of the recorded modalities and the feasibility of the MW elicitation protocol. Finally the MAUS dataset is now available for academic use (The MAUS dataset is available at IEEE Dataport: https://ieee-dataport.org/open-access/maus-dataset-mental-workload-assessment-n-back-task-using-wearable-sensor). Besides, we also presents a reproducible baseline system as a preliminary benchmark (The code of the baseline system on MAUS dataset is available on Github: https://github.com/rickwu11/MAUS\_dataset\_baseline\_system), which testing accuracy are 71.6 %, 66.7 %, and 59.9 % in ECG, fingertip PPG, wristband PPG, respectively.

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