Conference paper
Wearable Brain and Body Sensing for Multimodal Assessment of Cognitive Workload and Training
MP-HFM-334-13
NATO Human Factors & Medicine (HFM) Symposium, HFM-RSY-224 (Rome, Italy, 11 Oct 2021 - 12 Oct 2021)
31 Jan 2022
Abstract
The efficiency and safety of complex high precision human-machine systems such as in aerospace and robotic surgery are closely related to the cognitive readiness, ability to manage workload, and situational awareness of their operators. Accurate assessment of mental workload could help in preventing operator error and allow for pertinent intervention by predicting performance decline that can arise from either work overload or understimulation. Neuroergonomic approaches based on measures of human body and brain activity collectively can provide sensitive and reliable assessment of human mental workload in complex training and work environments. This paper outlines the potential of wearable brain and body imaging methods for the assessment of mental workload via neuro/physiological signals, and provides a study design for comparative evaluation of workload during multi-domain cognitive tasks with simultaneous multi-modal biosensors. Such comprehensive neuroergonomic assessment utilizing both neuroimaging and physiological monitoring can inform development of next generation neuroadaptive interfaces and training approaches for more efficient human-machine interaction and operator skill acquisition.
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Details
- Title
- Wearable Brain and Body Sensing for Multimodal Assessment of Cognitive Workload and Training
- Creators
- Jesse Mark - Drexel UniversityAdrian B Curtin - Drexel University, School of Biomedical Engineering, Science, and Health SystemsAmanda Kraft - Lockheed MartinMatthias Ziegler - Lockheed MartinHasan Ayaz (Corresponding Author) - Drexel University, School of Biomedical Engineering, Science, and Health Systems
- Publication Details
- MP-HFM-334-13
- Conference
- NATO Human Factors & Medicine (HFM) Symposium, HFM-RSY-224 (Rome, Italy, 11 Oct 2021 - 12 Oct 2021)
- Number of pages
- 18
- Grant note
- This research was supported in part by the US Air Force Research Laboratory’s Human Performance Sensing BAA call 002 under contract number FA8650-16-c-6764.
- Resource Type
- Conference paper
- Language
- English
- Academic Unit
- College of Arts and Sciences; Drexel Solutions Institute; School of Biomedical Engineering, Science, and Health Systems
- Identifiers
- 991019275313704721