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Review

Movement Sensing Opportunities for Monitoring Dynamic Cognitive States

by
Tad T. Brunyé
1,2,*,
James McIntyre
2,3,
Gregory I. Hughes
1 and
Eric L. Miller
2,3
1
U. S. Army DEVCOM Soldier Center, Natick, MA 01760, USA
2
Center for Applied Brain and Cognitive Sciences, Tufts University, Medford, MA 02155, USA
3
Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USA
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(23), 7530; https://doi.org/10.3390/s24237530
Submission received: 23 September 2024 / Revised: 7 November 2024 / Accepted: 23 November 2024 / Published: 25 November 2024
(This article belongs to the Special Issue Sensors for Human Movement Recognition and Analysis)

Abstract

In occupational domains such as sports, healthcare, driving, and military, both individuals and small groups are expected to perform challenging tasks under adverse conditions that induce transient cognitive states such as stress, workload, and uncertainty. Wearable and standoff 6DOF sensing technologies are advancing rapidly, including increasingly miniaturized yet robust inertial measurement units (IMUs) and portable marker-less infrared optical motion tracking. These sensing technologies may offer opportunities to track overt physical behavior and classify cognitive states relevant to human performance in diverse human–machine domains. We describe progress in research attempting to distinguish cognitive states by tracking movement behavior in both individuals and small groups, examining potential applications in sports, healthcare, driving, and the military. In the context of military training and operations, there are no generally accepted methods for classifying transient mental states such as uncertainty from movement-related data, despite its importance for shaping decision-making and behavior. To fill this gap, an example data set is presented including optical motion capture of rifle trajectories during a dynamic marksmanship task that elicits variable uncertainty; using machine learning, we demonstrate that features of weapon trajectories capturing the complexity of motion are valuable for classifying low versus high uncertainty states. We argue that leveraging metrics of human movement behavior reveals opportunities to complement relatively costly and less portable neurophysiological sensing technologies and enables domain-specific human–machine interfaces to support a wide range of cognitive functions.
Keywords: inertial measurement units; optical motion capture; cognitive state estimation; workload; uncertainty; machine learning; movement dynamics inertial measurement units; optical motion capture; cognitive state estimation; workload; uncertainty; machine learning; movement dynamics

Share and Cite

MDPI and ACS Style

Brunyé, T.T.; McIntyre, J.; Hughes, G.I.; Miller, E.L. Movement Sensing Opportunities for Monitoring Dynamic Cognitive States. Sensors 2024, 24, 7530. https://doi.org/10.3390/s24237530

AMA Style

Brunyé TT, McIntyre J, Hughes GI, Miller EL. Movement Sensing Opportunities for Monitoring Dynamic Cognitive States. Sensors. 2024; 24(23):7530. https://doi.org/10.3390/s24237530

Chicago/Turabian Style

Brunyé, Tad T., James McIntyre, Gregory I. Hughes, and Eric L. Miller. 2024. "Movement Sensing Opportunities for Monitoring Dynamic Cognitive States" Sensors 24, no. 23: 7530. https://doi.org/10.3390/s24237530

APA Style

Brunyé, T. T., McIntyre, J., Hughes, G. I., & Miller, E. L. (2024). Movement Sensing Opportunities for Monitoring Dynamic Cognitive States. Sensors, 24(23), 7530. https://doi.org/10.3390/s24237530

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