Dynamic difficulty using brain metrics of workload
Abstract
Dynamic difficulty adjustments can be used in human-computer systems in order to improve user engagement and performance. In this paper, we use functional near-infrared spectroscopy (fNIRS) to obtain passive brain sensing data and detect extended periods of boredom or overload. From these physiological signals, we can adapt a simulation in order to optimize workload in real-time, which allows the system to better fit the task to the user from moment to moment. To demonstrate this idea, we ran a laboratory study in which participants performed path planning for multiple unmanned aerial vehicles (UAVs) in a simulation. Based on their state, we varied the difficulty of the task by adding or removing UAVs and found that we were able to decrease error by 35% over a baseline condition. Our results show that we can use fNIRS brain sensing to detect task difficulty in real-time and construct an interface that improves user performance through dynamic difficulty adjustment.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
fNIRS has been demonstrated by HCI researchers to measure user cognitive workload in adaptive systems and is resilient to subject movement such as playing piano.
“Brain sensing using functional near-infrared spectroscopy (fNIRS) has recently been demonstrated by HCI researchers to measure user cognitive workload in adaptive systems [4,44,1] and is resilient to subject movement as might be seen while playing a piano [43].”
From Learn Piano with BACh: An Adaptive Learning Interface that Adjusts Task Difficulty Based on Brain State (2016) · cites this paper as [1]
Studies in HCI have successfully used fNIRS to measure and adapt to cognitive workload in real-time.
“Studies in HCI have successfully used fNIRS to measure and adapt to cognitive workload in real-time [44,1,2,55].”
From Learn Piano with BACh: An Adaptive Learning Interface that Adjusts Task Difficulty Based on Brain State (2016) · cites this paper as [1]
Cites
Working memory: Looking back and looking forward
Baddeley, A. (2003) · Nature Reviews Neuroscience · doi:10.1038/nrn1201 · Cited by 5,200 · see citing works
N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies.
Owen, A. M., McMillan, K. M., Laird, A. R., & Bullmore, E. (2005) · Human Brain Mapping · doi:10.1002/hbm.20131 · Cited by 3,560 · see citing works
Brainput: enhancing interactive systems with streaming fnirs brain input
Solovey, E., Schermerhorn, P., Scheutz, M., Sassaroli, A., Fantini, S., & Jacob, R. (2012) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2207676.2208372 · Cited by 86 · see citing works
Self-citationCapturing user engagement via psychophysiology: measures and mechanisms for biocybernetic adaptation
Fairclough, S. H., Gilleade, K., Ewing, K. C., & Roberts, J. (2013) · International Journal of Autonomous and Adaptive Communications Systems · doi:10.1504/ijaacs.2013.050694 · Cited by 60 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Survey on Measuring Cognitive Workload in Human-Computer Interaction
Kosch, T., Karolus, J., Zagermann, J., Reiterer, H., Schmidt, A., & Woźniak, P. W. (2023) · ACM Computing Surveys · doi:10.1145/3582272 · Cited by 304 · see citing works
“…52, 67, 70, 83, 86, 87, 100, 105, 106, 124, 130, 135, 138, 148, 150, 184, 197, 198, 207, 235, 243, 244, 268] EEG ERPs [23, 26, 59, 81, 184, 214, 227, 243] Hemodynamic Response (fNIRS) [3, 5, 32, 80, 96, 97, 104, 159, 161, 191, 192, 196, 221–223, 227, 261, 262] Electromagnetic Changes (fMRI) [220]”
Classifying driver workload using physiological and driving performance data: two field studies
Solovey, E. T., Zec, M., Garcia Perez, E. A., Reimer, B., & Mehler, B. (2014) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557068 · Cited by 182 · see citing works
Self-citationYuksel, B. F., Oleson, K. B., Harrison, L., Peck, E. M., Afergan, D., Chang, R., & Jacob, R. J. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858388 · Cited by 137 · see citing works
“Studies in HCI have successfully used fNIRS to measure and adapt to cognitive workload in real-time [44, 1, 2, 55].”
BackgroundMethodInfluentialSelf-citationSibi, S., Ayaz, H., Kuhns, D. P., Sirkin, D. M., & Ju, W. (2016) · 2016 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2016.7535420 · Cited by 56 · see citing works
Afergan, D., Shibata, T., Hincks, S. W., Peck, E. M., Yuksel, B. F., Chang, R., & Jacob, R. J. K. (2014) · Proceedings of the 27th annual ACM symposium on User interface software and technology · doi:10.1145/2642918.2647414 · Cited by 19 · see citing works
Self-citation
- Authors
- Afergan, D., Peck, E. M., Solovey, E. T., Jenkins, A., Hincks, S. W., Brown, E. T., Chang, R., & Jacob, R. J. K.
- Affiliations
- Tufts University (US)
- Drexel University (US)
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2014-04-26
- Pages
- 3797-3806
- OpenAlex
- W2144183573
- Cited by
- 148
- References
- 4
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 8 · 2023: 6 · 2022: 8 · 2021: 12 · 2020: 19 · 2019: 23 · 2018: 15 · 2017: 9 · 2016: 12 · 2015: 13 · 2014: 10
- FWCI
- 28.79
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- dynamic difficulty adjustment, functional near-infrared spectroscopy, boredom detection, cognitive overload, human-computer interaction, path planning, unmanned aerial vehicles, user performance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
