Eric C. Larson
Associate ProfessorComputer Science
Bobby B. Lyle School of Engineering
Southern Methodist University
twitter: @ec_larson
email: eclarson@lyle.smu.edu
CS Office: 451 Caruth Hall
Lyle School of Engineering
Caruth Hall
3145 Dyer Street, Suite 445
Dallas, TX 75205
SMU UbiComp Lab:
Johnson Square 189
Pilot Cognitive Workload Prediction
The following dataset is made available through federal grant FA7000-23-2-0003, funded by the United States Air Force Academy (USAFA), with title "Cognition-aware Computing Measurement via Biometric Sensing." The data was collected by Textron Aviation and processed by Southern Methodist University.
The dataset contains biometric information for a pilot as they fly through varying levels of turbulence in a simulator [1]. Numerous biometrics and features derived from the biometric data are available. Transfer learned features from the Wilson et al. model (BM3TX) are also included. Please see an explanation of these features in the paper referenced below [2]. Subjective scoring from the subjects for each trial is available. The cognitive load is captured using the NASA-TLX. The objective of this dataset is to use the biometric sensor streams to predict the reported task workload. This task workload has been processed in several different ways. One can use any of these measures as the ground truth label of workload. These measures are described more fully in the README.
The files can be downloaded in two compressed files: (1) Table Data: This has been already preprocessed such that each row in the Table represents one trial of one pilot. (2) Windowed Samples: This form of the dataset contains more detailed information and raw biometrics samples for each pilot and each trial.
These datasets can be downloaded and used for any academic purposes. When using the Dataset, please cite the following papers:
The following dataset is made available through federal grant FA7000-23-2-0003, funded by the United States Air Force Academy (USAFA), with title "Cognition-aware Computing Measurement via Biometric Sensing." The data was collected by Textron Aviation and processed by Southern Methodist University.
The dataset contains biometric information for a pilot as they fly through varying levels of turbulence in a simulator [1]. Numerous biometrics and features derived from the biometric data are available. Transfer learned features from the Wilson et al. model (BM3TX) are also included. Please see an explanation of these features in the paper referenced below [2]. Subjective scoring from the subjects for each trial is available. The cognitive load is captured using the NASA-TLX. The objective of this dataset is to use the biometric sensor streams to predict the reported task workload. This task workload has been processed in several different ways. One can use any of these measures as the ground truth label of workload. These measures are described more fully in the README.
The files can be downloaded in two compressed files: (1) Table Data: This has been already preprocessed such that each row in the Table represents one trial of one pilot. (2) Windowed Samples: This form of the dataset contains more detailed information and raw biometrics samples for each pilot and each trial.
- Table data and example code (<1MB): TableDataAndCode.zip
- Windowed Samples, per Pilot (~310 MB): WindowedSamples.zip
These datasets can be downloaded and used for any academic purposes. When using the Dataset, please cite the following papers:
- [1] Barnett, N., Nagrecha, S., Glover, M., Harper, C., Wilson, J., Maher, J., & Larson, E. C. (2025). Generalizing Classification of Pilot Workload: Transfer Learning versus a JEPA-Inspired Transformer Architecture. International Journal of Aviation, Aeronautics, and Aerospace, 12(1). Full Text from IJAAA
- [2] Justin C. Wilson, Suku Nair, Sandro Scielzo, and Eric C. Larson. 2021. Objective Measures of Cognitive Load Using Deep Multi-Modal Learning: A Use-Case in Aviation. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 5, 1, Article 40 (March 2021), 35 pages. Full Text from IMWUT
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