Presented at the 1st International Symposium on Human Mental Workload: Models and Applications (H-WORKLOAD 2017), Dublin, Ireland (June 2017)
Across many careers, individuals face alternating periods of high and low attention and cognitive workload can impair cognitive function and undermine job performance. We have designed and are developing an unobtrusive system to Monitor, Extract, and Decode Indicators of Cognitive Workload (MEDIC) in naturalistic, high-motion environments. MEDIC is designed to warn individuals, teammates, or supervisors when steps should be taken to augment cognitive readiness. We first designed and manufactured a forehead sensor device that includes a custom fNIRS sensor and a three-axis accelerometer designed to be mounted on the inside of a baseball cap or headband, or standard issue gear such as a helmet or surgeon’s cap. Because the conditions under which MEDIC is designed to operate are more strenuous than typical research efforts assessing cognitive workload, motion artifacts in our data were a persistent issue. Results show wavelet-based filtering improved data quality to salvage data from even the highest-motion conditions. MARA spline motion correction did not further improve data quality. Our testing shows that each of the methods is extremely effective in reducing the effects of motion transients present in the data. In combination, they are able to almost completely remove the transients in the signal while preserving cardiac and low frequency information in the signal which was previously unrecoverable. This has substantially improved the stability of the physiological measures produced by the sensors in high noise conditions.
1 Charles River Analytics
2 McLean Hospital/Harvard Medical School
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