Subjective Workload Assessment Technique (SWAT) in Real Time: Affordable Methodology to Continuously Assess Human Operators’ Workload

Zak, Yuval; Parmet, Yisrael; Oron-Gilad, Tal

doi:10.1109/smc42975.2020.9283168

Cited by 9 publications

(9 citation statements)

References 30 publications

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“…Finally, the “noisy” signal of the measured grip force (as illustrated in Figure 4 ) may interfere with the analysis of the state of the driver. Therefore, other data processing methods (e.g., fast Fourier transform) should be considered if real-time assessment is required ( Zak et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Grip Force on Steering Wheel as a Measure of Stress

et al. 2021

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Driver performance is crucial for road safety. There is a relationship between performance and stress such that too high or too low stress levels (usually characterized by stressful or careless driving, respectively) impair driving quality. Therefore, monitoring stress levels can improve the overall performance of drivers by providing either an alert or intervention when stress levels are sub-optimal. Commonly used stress measures suffer from several shortcomings, such as time delays in indication and invasiveness of sensors. Grip force is a relatively new measure that shows promising results in measuring stress during psychomotor tasks. In driving, grip force sensor is non-invasive and transparent to the end user as drivers must continuously grip the steering wheel. The aim of the current research is to examine whether grip force can be used as a useful measure of stress in driving tasks. Twenty-one participants took part in a field experiment in which they were required to brake the vehicle in various intensities. The effects of the braking intensity on grip force, heart rate, and heart rate variability were analyzed. The results indicate a significant correlation between these three parameters. These results provide initial evidence that grip force can be used to measure stress in driving tasks. These findings may have several applications in the field of stress and driving research as well as in the vehicle safety domain.

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Section: Discussionmentioning

confidence: 99%

Grip Force on Steering Wheel as a Measure of Stress

et al. 2021

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“…Then he was asked to perform the preliminary task to acquire a perception of the cognitive workload, as required by the SWAT technique ( Reid et al, 1988 ). Once completed, the participant went through the workload setup phase, where he had to complete the missions of the first scenario while continuously answering, every 10–30 seconds, the SWAT questionnaire regarding his current perceived workload ( Reid & Nygren, 1988 ; Zak et al, 2020e ). The C2 map during the setup phase did not include a filter and showed all information items.…”

Section: Empirical Evaluation and Resultsmentioning

confidence: 99%

“…The raw data collected via the UCES included the time (in milliseconds), the current stage, the filter protocol, the number of items shown on the map, and the joystick’s state on its two axes. Cognitive workload was derived from the joystick’s state using the ML-based SWAT-RT technique ( Zak et al, 2020e ). Since each participant may experience workload differently, the scores’ scale differs among participants.…”

Section: Empirical Evaluation and Resultsmentioning

confidence: 99%

“…In a setup phase during a designated scenario, the participant's cognitive workload score was recorded every 10-30 seconds using the SWAT (Subjective Workload Assessment Technique; Reid & Nygren, 1988) and compared with his joystick performance. Then, following the Zak et al (2020e) SWAT in Real Time (SWAT-RT) guidelines, the joystick movements' magnitude and variability was used to create an ML model that continuously assessed the operator's workload at any given time. One of the experimental scenarios was used for the setup phase and the remaining two Filter "1"-Positive Correlation Filter "2"-Negative Correlation Filter "1"-Positive Correlation…”

Section: Continuous Cognitive Workload Assessmentmentioning

confidence: 99%

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Facilitating the Work of Unmanned Aerial Vehicle Operators Using Artificial Intelligence: An Intelligent Filter for Command-and-Control Maps to Reduce Cognitive Workload

2022

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Objective Evaluating the ability of a Gibsonian-inspired artificial intelligence (AI) algorithm to reduce the cognitive workloads of military Unmanned Aerial Vehicle (UAV) operators. Background Military UAV operators use the command-and-control (C2) map for developing mission-relevant situation awareness (SA). Yet C2 maps are overloaded with information, mostly irrelevant to the mission, causing operators to neglect the map altogether. To reduce irrelevant information, an intelligent filtering algorithm was developed. Here we evaluate its effectiveness in reducing operators’ cognitive workloads. Method Two-stage operational scenarios were conducted with professional ex-military UAV operators, using two filter protocols and a no-filter control. High-end real-time techniques were used to continuously assess workload from muscle behavior and machine learning models. Results Lower cognitive workload was found when applying the algorithm’s protocols, especially when fatigue started to accumulate (Stage II). However, concerns about the quality of SA arose. Conclusion The algorithm was positively evaluated for its ability to reduce operators’ cognitive workloads. More evaluations of operators’ SA are required. Application The algorithm demonstrates the possibility of integrating AI to improve human performance in complex systems, and can be applied to other domains where spatial-temporal information needs to be contextually filtered in real time.

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“…Quantifying the level of cognitive workload in real-time has real-world applications across several domains, such as preventing distracted driving [ 3 ], rating pilots’ performance [ 4 ] and providing individualized return-to-learn guidelines following mild traumatic brain injury [ 5 ]. Though questionnaire surveys and observation of human behavior can be used to estimate cognitive workload, such estimates are subjective (hence, subject to bias) and are not available on a continuous, real-time basis [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Cognitive Workload Using a Non-Contact Magnetocardiography (MCG) Wearable Sensor

Wang

Zhu

Kaur

et al. 2022

Sensors

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Quantifying cognitive workload, i.e., the level of mental effort put forth by an individual in response to a cognitive task, is relevant for healthcare, training and gaming applications. However, there is currently no technology available that can readily and reliably quantify the cognitive workload of an individual in a real-world environment at a seamless way and affordable price. In this work, we overcome these limitations and demonstrate the feasibility of a magnetocardiography (MCG) sensor to reliably classify high vs. low cognitive workload while being non-contact, fully passive and low-cost, with the potential to have a wearable form factor. The operating principle relies on measuring the naturally emanated magnetic fields from the heart and subsequently analyzing the heart rate variability (HRV) matrix in three time-domain parameters: standard deviation of RR intervals (SDRR); root mean square of successive differences between heartbeats (RMSSD); and mean values of adjacent R-peaks in the cardiac signals (MeanRR). A total of 13 participants were recruited, two of whom were excluded due to low signal quality. The results show that SDRR and RMSSD achieve a 100% success rate in classifying high vs. low cognitive workload, while MeanRR achieves a 91% success rate. Tests for the same individual yield an intra-subject classification accuracy of 100% for all three HRV parameters. Future studies should leverage machine learning and advanced digital signal processing to achieve automated classification of cognitive workload and reliable operation in a natural environment.

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Subjective Workload Assessment Technique (SWAT) in Real Time: Affordable Methodology to Continuously Assess Human Operators’ Workload

Cited by 9 publications

References 30 publications

Grip Force on Steering Wheel as a Measure of Stress

Grip Force on Steering Wheel as a Measure of Stress

Facilitating the Work of Unmanned Aerial Vehicle Operators Using Artificial Intelligence: An Intelligent Filter for Command-and-Control Maps to Reduce Cognitive Workload

Quantifying Cognitive Workload Using a Non-Contact Magnetocardiography (MCG) Wearable Sensor

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