Dynamic Difficulty Using Brain Metrics of Workload

被引：92

作者：

Afergan, Daniel ^{[1
]}

Peck, Evan M. ^{[1
]}

Solovey, Erin T. ^{[2
]}

Jenkins, Andrew ^{[1
]}

Hincks, Samuel W. ^{[1
]}

Brown, Eli T. ^{[1
]}

Chang, Remco ^{[1
]}

Jacob, Robert J. K. ^{[1
]}

机构：

[1] Tufts Univ, Medford, MA 02155 USA

[2] Drexel Univ, Philadelphia, PA 19104 USA

来源：

32ND ANNUAL ACM CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2014) | 2014年

基金：

美国国家科学基金会;

关键词：

BCI; passive brain-computer interface; dynamic difficulty; fNIRS; near-infrared spectroscopy; workload; UAV; WORKING-MEMORY; PERFORMANCE; BACK; BOLD;

D O I：

10.1145/2556288.2557230

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：3797 / 3806

页数：10

共 44 条

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