Physical and cognitive demands associated with police in-vehicle technology use: an on-road case study

Zahabi, Maryam; Shahini, Farzaneh; Wei, Yin; Zhang, Xudong

doi:10.1080/00140139.2021.1960429

Cited by 13 publications

(5 citation statements)

References 38 publications

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“…The raw kinematics signals were transformed to segmental anatomical coordinate systems, and then, segmental flexion/extension angular velocity and angles and linear acceleration (gravity removed) were extracted. The sEMG data were first filtered using a fourth-order band-pass Butterworth filter (10-400Hz) (Zahabi et al, 2021). Then, the data were rectified and smoothed using the Root Mean Square (RMS) with a moving window of 125 ms.…”

Section: Feature Extractionmentioning

confidence: 99%

Towards Real-Time Minimum-Input Prediction of Lumbar Moment Based on Flexible Sensors and Machine Learning

Wei

Chen

Reddy

et al. 2022

Proceedings of the Human Factors and Ergonomics Society Annual

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Knowledge of low-back loading is essential for understanding and mitigating the risk of low-back overexertion injuries. Conventional data acquisition methods for estimating joint loading are limited to laboratory settings, whereas wearable sensors can provide a mobile and cost-effective alternative. This study investigated the feasibility of learning prediction of L5S1 flexion moment based on kinematics and electromyography (EMG) measurements from flexible sensors. Four machine learning methods were compared, and different subsets of sensor inputs were explored. Results indicated that the support vector machine (SVM) method outperformed others, and a subset of four out of seven sensor locations, namely sacrum, thigh, shank, and thoracic erector spinae, yielded the best predictive accuracy. The study demonstrates that machine learning can unlock the potential of mobile miniaturized flexible sensors in field biomechanics or ergonomics studies.

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Section: Feature Extractionmentioning

confidence: 99%

Towards Real-Time Minimum-Input Prediction of Lumbar Moment Based on Flexible Sensors and Machine Learning

Wei

Chen

Reddy

et al. 2022

Proceedings of the Human Factors and Ergonomics Society Annual

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“…CW measurement techniques are typically categorized into four groups including: physiological measures, subjective rating scales, task performance measures (Eggemeier & Wilson, 1991), and cognitive performance modeling (CPM). Physiological measures can directly and continuously measure CW based on the changes in pupillometry data (Zahabi et al, 2022), heart rate (HR) (McDonald et al, 2019), respiratory rate (RR) (McDonald et al, 2019), and skin conductance (SC) (Singh et al, 2013). CW can also be measured subjectively by measures such as the NASA Task Load Index (NASA-TLX) (Hart, 1986) or objectively using task performance responses such as task completion time (Shahini et al, 2021).…”

Section: Current Approaches To Measure Cognitive Workloadmentioning

confidence: 99%

Measuring Cognitive Workload of Novice Law Enforcement Officers in a Naturalistic Driving Study

Wozniak

Park

Nunn

et al. 2022

Proceedings of the Human Factors and Ergonomics Society Annual

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There is a large amount of variation between novices and experts in their cognitive workload when performing tasks. A naturalistic pilot study was conducted with nine novice law enforcement officers (nLEOs) to determine how their use of in-vehicle technology affected their cognitive workload during their normal patrols. Physiological data were collected using a novel synchronization process for naturalistic driving studies, allowing heart rate variability and eye tracking measurements to be synchronized together and directly compared to subjective workload levels. It was found that nLEOs have average or higher workload compared to experienced officers and the general population when they are on duty. Future studies can utilize the approaches and findings of this pilot study for conducting naturalistic driving studies and developing cognitive performance models for novice users.

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“…Law enforcement officer (LEO) vehicles are equipped with several in-vehicle technologies such as cameras, advanced driver assistance systems, mobile computer terminals, radios, and radar, which assist LEOs in accessing real-time information and improve their safety and efficiency in their day-to-day work (Zahabi et al, 2022). However, using these technologies designed to assist and enable LEOs to perform their jobs safely make their vehicles complex and vulnerable to cyberattacks.…”

Section: Introductionmentioning

confidence: 99%

Law Enforcement Perspectives on Police Vehicle Cybersecurity

Roberts,

Zahabi,

McCarthy

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Cyberattacks on law enforcement officers’ vehicles can compromise and disrupt emergency response, yet there is little knowledge of how law enforcement officers understand and respond to cybersecurity concerns within their work vehicles. This study gathered data on law enforcement officers’ perceptions of vehicle cybersecurity. A survey study was conducted with 80 law enforcement officers, focusing on gathering information to quantify the relationship between their experience as an officer, their cybersecurity experience, and their perception of vehicle cybersecurity. Overall, there was a lack of understanding and knowledge regarding vehicle cybersecurity. Results also revealed a small relationship between how many hours an officer spends in the vehicle and vehicle cybersecurity and an even stronger relationship between the officer’s cybercrime experience and their perceptions of vehicle cybersecurity. With appropriate interventions (e.g., training), there is an opportunity to impact law enforcement behavior to positively promote vehicle cybersecurity resiliency.

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Physical and cognitive demands associated with police in-vehicle technology use: an on-road case study

Cited by 13 publications

References 38 publications

Towards Real-Time Minimum-Input Prediction of Lumbar Moment Based on Flexible Sensors and Machine Learning

Towards Real-Time Minimum-Input Prediction of Lumbar Moment Based on Flexible Sensors and Machine Learning

Measuring Cognitive Workload of Novice Law Enforcement Officers in a Naturalistic Driving Study

Law Enforcement Perspectives on Police Vehicle Cybersecurity

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