期刊
32ND ANNUAL ACM CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2014)
卷 -, 期 -, 页码 3797-3806出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2556288.2557230
关键词
BCI; passive brain-computer interface; dynamic difficulty; fNIRS; near-infrared spectroscopy; workload; UAV
资金
- NSF [IIS1065154, IIS-1218170]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1065154] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1218170] Funding Source: National Science Foundation
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 errors 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.
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