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

arXiv: https://arxiv.org/abs/2111.02561 · HTML: https://mlsanity.com/paper/2111.02561/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2021.

## Built on by (most cited first, 7 of 7)

- [Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals](https://mlsanity.com/paper/2412.09758/) (2024)
- [Computational Analysis of Stress, Depression and Engagement in Mental Health: A Survey](https://mlsanity.com/paper/2403.08824/) (2024)
- [Wearable Foundation Models Should Go Beyond Static Encoders](https://mlsanity.com/paper/2603.19564/) (2026)
- [Generalist vs Specialist Time Series Foundation Models: Investigating Potential Emergent Behaviors in Assessing Human Health Using PPG Signals](https://mlsanity.com/paper/2510.14254/) (2025)
- [Data Fusion in Neuromarketing: Multimodal Analysis of Biosignals, Lifecycle Stages, Current Advances, Datasets, Trends, and Challenges](https://mlsanity.com/paper/2209.00993/) (2022)
- [Reliable Physiological Monitoring on the Wrist Using Generative Deep Learning to Address Poor Skin-Sensor Contact](https://mlsanity.com/paper/2504.02735/) (2025)
- [REVELIO -- Universal Multimodal Task Load Estimation for Cross-Domain Generalization](https://mlsanity.com/paper/2509.01642/) (2025)

## Abstract (arXiv)

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/MAUSdatasetbaselinesystem), which testing accuracy are 71.6 %, 66.7 %, and 59.9 % in ECG, fingertip PPG, wristband PPG, respectively.