Computers would soon be able to to respond to users’ thoughts of frustration caused by too much work or boredom caused by too little work, thanks to new techniques being developed by Tufts University researchers.
By applying non-invasive and easily portable imaging technology in new ways, the team hopes to gain real-time insight into the brain’s more subtle emotional cues and help boost productivity.
“New evaluation techniques that monitor user experiences while working with computers are increasingly necessary,” said Robert Jacob, computer science professor and researcher. “One moment a user may be bored, and the next moment, the same user may be overwhelmed. Measuring mental workload, frustration and distraction is typically limited to qualitatively observing computer users or to administering surveys after completion of a task, potentially missing valuable insight into the users’ changing experiences."
The researchers are studying functional near-infrared spectroscopy (fNIRS) technology that uses light to monitor brain blood flow as a proxy for workload stress a user may experience when performing an increasingly difficult task.
National Science Foundation is funding the interdisciplinary team to the tune of $445,000, allowing them to incorporate real-time biomedical data with machine learning to produce a more in-tune computer user experience.
“fNIRS is an emerging non-invasive, lightweight imaging tool which can measure blood oxygenation levels in the brain,” said biomedical engineering professor Sergio Fantini.
The fNIRS device, which looks like a futuristic headband, uses laser diodes to send near-infrared light through the forehead at a relatively shallow depth of two to three centimeters to interact with the brain’s frontal lobe.
Light usually passes through the body’s tissues, except when it encounters oxygenated or deoxygenated hemoglobin in the blood. Light waves are absorbed by the active, blood-filled areas of the brain and any remaining light is diffusely reflected to the fNIRS detectors.
In the initial experiments, Jacob and Fantini’s groups determined how accurately fNIRS could register users’ workload.
Test subjects viewed a multicolored cube consisting of eight smaller cubes with two, three or four different colors, while wearing the fNIRS device, and counted the number of colored squares in a series of 30 tasks as the cube rotated onscreen.
The fNIRS device and subsequent user surveys reflected greater difficulty as users kept track of increasing numbers of colors and the fNIRS data agreed with user surveys up to 83 percent of the time.
The Tufts group will present its initial results on using fNIRS to detect the user workload experience at the Association for Computing Machinery (ACM) symposium on user interface software and technology to be held next week.
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