Multi-modal physiological sensing approach for distinguishing high workload events in remotely piloted aircraft simulation

Yu, Denny; Antonik, Chantale Wilson; Webber, Frederick; Watz, Eric; Bennett, Winston

doi:10.1007/s42454-020-00016-w

Cited by 3 publications

(1 citation statement)

References 56 publications

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“…A systematic review conducted by Charles and Nixon ( 2019 ) detailed 58 journal articles and demonstrated the empirical basis for using physiological sensors in quantifying MWL across a variety of domains. Studies in teleoperation tasks, such as robotics and drone operations, have also shown that physiological sensors can assess workload in those domains (Dias et al, 2018 ; Yu et al, 2019 ; Zhou et al, 2020 ). However, previous studies have primarily focused on the sensors' effectiveness in detecting large changes or classifying high vs. low MWL, limiting the granularity of existing models (their ability to detect fine, multi-level changes in workload).…”

Section: Introductionmentioning

confidence: 99%

Granular estimation of user cognitive workload using multi-modal physiological sensors

Wang,

Stevens,

Bennett

et al. 2024

Front. Neuroergonomics

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Mental workload (MWL) is a crucial area of study due to its significant influence on task performance and potential for significant operator error. However, measuring MWL presents challenges, as it is a multi-dimensional construct. Previous research on MWL models has focused on differentiating between two to three levels. Nonetheless, tasks can vary widely in their complexity, and little is known about how subtle variations in task difficulty influence workload indicators. To address this, we conducted an experiment inducing MWL in up to 5 levels, hypothesizing that our multi-modal metrics would be able to distinguish between each MWL stage. We measured the induced workload using task performance, subjective assessment, and physiological metrics. Our simulated task was designed to induce diverse MWL degrees, including five different math and three different verbal tiers. Our findings indicate that all investigated metrics successfully differentiated between various MWL levels induced by different tiers of math problems. Notably, performance metrics emerged as the most effective assessment, being the only metric capable of distinguishing all the levels. Some limitations were observed in the granularity of subjective and physiological metrics. Specifically, the subjective overall mental workload couldn't distinguish lower levels of workload, while all physiological metrics could detect a shift from lower to higher levels, but did not distinguish between workload tiers at the higher or lower ends of the scale (e.g., between the easy and the easy-medium tiers). Despite these limitations, each pair of levels was effectively differentiated by one or more metrics. This suggests a promising avenue for future research, exploring the integration or combination of multiple metrics. The findings suggest that subtle differences in workload levels may be distinguishable using combinations of subjective and physiological metrics.

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Section: Introductionmentioning

confidence: 99%

Granular estimation of user cognitive workload using multi-modal physiological sensors

Wang,

Stevens,

Bennett

et al. 2024

Front. Neuroergonomics

Self Cite

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Cognitive Workload Estimation Using LSTM and Ensemble Learning on Multimodal Physiological Data

Singh,

Duman,

Schulte

2024

2024 IEEE International Symposium on Safety Security Rescue Robotics (SSRR)

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Enriched Optimization Algorithm for Effective Skin Disease Prediction Using Soft Computing Techniques

R¹,

Dhanagopal²,

Kumar

2021

j med imaging hlth inform

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In recent years, a usual worldwide problem is a skin disease—the diagnostics of infection and skin disease prediction based on the data mining techniques. The precise and cost-effective treatments obtain a technologybased data mining system that can consider making the right decision. Depends on data, there are 34 UCI datasets have in the skin disease prediction. All of the datasets are not much important when predicting the skin disease problem. In this study, the essential datasets to be analyzed because they only give the best accuracy in skin disease prediction. For an outstanding selection of allocation, to propose a novel feature selection approaches, Enriched Fruitfly Optimization Algorithm (EFOA), and Ensemble Classifiers that are helps for an early stage of skin disease prediction. A hybrid technique through the three essential hybrid feature selection approaches such as Chi-Square method, Information Gain method, and Principal Component Analysis (PCA) methods that are combined for better feature selection results. Based on the skin disease dataset, the resultant feature selection approach generated the reduced data subset. Then, the Enriched Fruitfly Optimization Algorithm (EFOA) offers the optimization of reduced data subset. Here, the accuracy estimation is the vital factor to optimize the effective and best prediction of skin disease affected regions. Afterward, the classification performs to classify the EFOA based optimized result by using the six different classification methods. Where, the classification helps to analyze the optimized results, which offers the better classification procedure. To predict the base learner’s performance, to utilize the Naive Bayesian, K-Nearest Neighbour, Decision Tree, Support Vector Machine, Random Forest, and Multilayer Perceptron (MLP) to classify the optimized result. Then, the ensemble techniques used to analyze the classifier’s results through the 3 different methods like Bagging, Boosting, Stacking, added on the base learners to improve the proposed work. Based on the performance, the base learners’ performance is larger than the input dataset. The base learner’s parameters are essential to calculate the accuracy of skin disease prediction performance. The performance of the proposed method will take and compare to each base learner, and the performance shows the accurate skin disease prediction improvement with other existing methods.

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Multi-modal physiological sensing approach for distinguishing high workload events in remotely piloted aircraft simulation

Cited by 3 publications

References 56 publications

Granular estimation of user cognitive workload using multi-modal physiological sensors

Granular estimation of user cognitive workload using multi-modal physiological sensors

Cognitive Workload Estimation Using LSTM and Ensemble Learning on Multimodal Physiological Data

Enriched Optimization Algorithm for Effective Skin Disease Prediction Using Soft Computing Techniques

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