Cognitive workload classification of law enforcement officers using physiological responses.

abstract

• Motor vehicle crashes (MVCs) are a leading cause of death for law enforcement officers (LEOs) in the U.S. LEOs and more specifically novice LEOs (nLEOs) are susceptible to high cognitive workload while driving which can lead to fatal MVCs. The objective of this study was to develop a machine learning algorithm (MLA) that can estimate cognitive workload of LEOs while performing secondary tasks in a patrol vehicle. A ride-along study was conducted with 24 nLEOs. Participants performed their normal patrol operations while their physiological responses such as heartrate, eye movement, and galvanic skin response were recorded using unobtrusive devices. Findings suggested that the random forest algorithm could predict cognitive workload with relatively high accuracy (>70%) given that it was entirely reliant on physiological signals. The developed MLA can be used to develop adaptive in-vehicle technology based on real-time estimation of cognitive workload, which can reduce the risk of MVCs in police operations.

authors

• Zahabi, Maryam

published proceedings

• Appl Ergon

author list (cited authors)

• Wozniak, D., & Zahabi, M.

complete list of authors

• Wozniak, David||Zahabi, Maryam

publication date

• May 2024

publisher

• Elsevier  Publisher

published in

• Applied Ergonomics  Journal

keywords

• Adaptive Technology
• Law Enforcement Officers
• Machine Learning Algorithm

PubMed Central ID

• 38733659

Digital Object Identifier (DOI)

• 10.1016/j.apergo.2024.104305

start page

• 104305

end page

• 104305

volume

• 119

URL

• http://dx.doi.org/10.1016/j.apergo.2024.104305