Validation of a task demand measure for predicting mental workloads of physical therapists

Liang, Sheau Farn Max; Rau, Chi Lun; Tsai, Pei Fang Jennifer; Chen, Wen Shiuan

doi:10.1016/j.ergon.2014.08.002

Cited by 21 publications

(13 citation statements)

References 28 publications

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“…Furthermore,

B_{k j}

is the amount of behavior element

j

in task

k

, and

B_{k j} = 1

when behavior element

j

exists, otherwise

B_{k j} = 0

L_{k j}

indicates the information demand of behavior element

j

in task

k

, and its value can be assessed referring to the McCracken‐Aldrich scale (Liang et al, 2014; McCracken & Aldrich, 1984). In addition,

m = j + 1

;

E_{k j m}

is the conflict value of concurrent behavior element

j

and element m in task

k

, and it can be identified by the conflict matrix (Schuck, 1996; Wickens, 2008b).…”

Section: Sadc Modelmentioning

confidence: 99%

An approach to situation awareness (SA) assessment in flight simulation: SA dynamic circulation (SADC) model

Feng

Wanyan

Liu

et al. 2021

Hum Ftrs & Erg Mfg Svc

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Accurate prediction and evaluation of pilot's situation awareness (SA) are of great importance for ensuring flight safety. In the present study, an SA dynamic circulation (SADC) model is developed for describing the dynamic generation and update processes of pilot's SA in‐flight environments. In the SADC model, five cognitive statuses of SA, which constitute the dynamic circulation in a closed‐loop network, are put forward on a framework of adaptive control of thought rational‐queuing network. By introducing the progressive triggers of the low and high SA, pilot's SA covering three levels is computed using conditional probability. In addition, flight simulations based on four typical flight scenarios were performed to verify the usability of the SADC model, and multiple methods including the subjective evaluation, situation present assessment method (SPAM), and physiological measurement were adopted for SA measurement. According to the results, the rationality of the SADC model is validated due to the significant correlations between SA theoretical prediction value and SPAM response time as well as 10D‐SART score. Besides, in our study, the SPAM response time and 10D‐SART score show an acceptable sensitivity to SA measurement, however, physiological indicators of respiratory rate and standard deviation of normal to normal RR intervals are proved more sensitive to workload rather than SA. The SADC model is expected to be applied in the quantitative characterization of pilot's SA as a new auxiliary analysis tool during cockpit display interface design and flight training strategy optimization.

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“…Furthermore,

B_{k j}

is the amount of behavior element

j

in task

k

, and

B_{k j} = 1

when behavior element

j

exists, otherwise

B_{k j} = 0

L_{k j}

indicates the information demand of behavior element

j

in task

k

, and its value can be assessed referring to the McCracken‐Aldrich scale (Liang et al, 2014; McCracken & Aldrich, 1984). In addition,

m = j + 1

;

E_{k j m}

is the conflict value of concurrent behavior element

j

and element m in task

k

, and it can be identified by the conflict matrix (Schuck, 1996; Wickens, 2008b).…”

Section: Sadc Modelmentioning

confidence: 99%

An approach to situation awareness (SA) assessment in flight simulation: SA dynamic circulation (SADC) model

Feng

Wanyan

Liu

et al. 2021

Hum Ftrs & Erg Mfg Svc

View full text Add to dashboard Cite

show abstract

“…Each task unit contains one or several specific behaviors [15]. The behavior of task segmentation is executed by different parts and actions of the operator [16]. The McCracken-Aldrich Mental Stress Assessment Scale [5] divides behaviors into four channels: visual, auditory, cognitive, and motor.…”

Section: Task Type Correlation Coefficient Solutionmentioning

confidence: 99%

“…Bin and Salvendy [13,14] proposed a dynamic time conceptual model for mental workload prediction, including instantaneous mental workload (IMW), average mental workload (AMW), cumulative mental workload (CMW), maximum mental workload, and total mental workload (TMW), independent of a particular field of mission scenario. Laughery et al [15,16] conducted a task analysis method to calculate mental workload by decomposing complex tasks into multiple simple behavioral elements and connecting the elements hierarchically and logically. However, the massive task was difficult to analyze in detail.…”

Section: Introductionmentioning

confidence: 99%

Pilots’ mental workload prediction based on timeline analysis

Liu

Wanyan

Xiao

et al. 2020

THC

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BACKGROUND: The aircraft cockpit is a highly intensive human-computer interaction system, and its design directly affects flight safety. OBJECTIVE: To optimize the display interface design in complex flight tasks, the present study aimed to propose a dynamic conceptual framework and a timeline task analysis method for the quantization of the dynamic time effect of mental workload and the influencing factors of task types in the mental workload prediction model. METHODS: The multi-factor mental workload prediction model based on attention resource allocation was integrated to establish the dynamic prediction model of mental workload. The ergonomics simulation experiment was carried out by recording the data on the performance of embedded subtasks, National Aeronautics and Space Administration-Task Load Index (NASA-TLX) subjective evaluation, and eye tracking. RESULTS: The results indicated that the prediction model had a good prediction accuracy and effectiveness under different simulated interfaces and complex tasks, and the real-time monitoring of pilots’ mental workload state was realized. CONCLUSION: In conclusion, the prediction model and the experimental method could be applied to avoid the overload of the pilot throughout the flight phase by optimizing the display interface and adjusting the flight task.

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“…Liang et al. [30] performed CTA and rated the cognitive workload demand of tasks using the VACP rating scale [31] and empirically validated the ratings with data obtained via NASA-TLX. While this work did contain a validation of standardized classifications for task cognitive workload, metrics for validation were limited to subjective survey data collected post hoc.…”

Section: Introductionmentioning

confidence: 99%

Cognitive task analysis and workload classification

2021

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Automation can be utilized to relieve humans of difficult and repetitive tasks in many domains, presenting the opportunity for safer and more efficient systems. This increase in automation has led to new supervisory roles for human operators where humans monitor feedback from autonomous systems and provide input when necessary. Optimizing these roles requires tools for evaluation of task complexity and resulting operator cognitive workload. Cognitive task analysis is a process for modeling the cognitive actions required of a human during a task. This work presents an enhanced version of this process: Cognitive Task Analysis and Workload Classification (CTAWC). The goal of developing CTAWC was to provide a standardized process to decompose cognitive tasks in enough depth to allow for precise identification of sources of cognitive workload. CTAWC has the following advantages over conventional CTA methodology: Integrates standard terminology from existing taxonomies for task classification to describe expected operator cognitive workload during task performance. Provides a framework to evaluate adequate cognitive depth when decomposing cognitive tasks. Provides a standard model upon which to build an empirical study to evaluate task complexity.

show abstract

Validation of a task demand measure for predicting mental workloads of physical therapists

Cited by 21 publications

References 28 publications

An approach to situation awareness (SA) assessment in flight simulation: SA dynamic circulation (SADC) model

An approach to situation awareness (SA) assessment in flight simulation: SA dynamic circulation (SADC) model

Pilots’ mental workload prediction based on timeline analysis

Cognitive task analysis and workload classification

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