Examination of Evaluation Method on Human Error During Work by Bioinstrumentation

Kishimoto, Taro; Yoshida, Reiji; Tobe, Yoshito; Sugaya, Midori

doi:10.1109/rew.2019.00019

Cited by 2 publications

(3 citation statements)

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“…HRV and EEG indexes as described in the following sections were chosen from previous studies by Lin et al [ 24 ] and Kishimoto et al [ 25 ], as they investigated the estimation of psychological states related to human error. Since the combination of HRV indexes and EEG indexes have been successfully used for emotion estimation [ 18 , 19 , 44 ], we proposed using them in combination in order to estimate psychological states related to human error more accurately.…”

Section: Experimental Methodsmentioning

confidence: 99%

“…However, the model cannot be generalized because it used EEG signals obtained from only a limited number of brain regions. Kishimoto et al surveyed the influence of discomfort with workplaces on human errors using NASA-TLX [ 36 ] as a criterion for workplace environments to suggest a specific correspondence between the NASA-TLX and HRV indexes as the human error occurred and clarified the characteristics of HRV indexes at the moment of human error occurrence [ 25 ]. However, they cannot be utilized for preventing human error, which also requires the assessment of psychological states “before” the human error (i.e., the human pre-error state) occurrence.…”

Section: Previous Studies On Human Errors From Affective Computingmentioning

confidence: 99%

“…However, these studies did not clarify the relationship between cognitive load and human error and thus could not predict human error probability. Some studies have focused on the modeling for predicting human error occurrence with biological signals (e.g., HRV and EEG) [ 24 , 25 ]. However, the data analysis results only covered a limited number of brain regions or did not assess psychological states “before” the human error (i.e., the human pre-error state) occurrence, so the results could not be utilized to prevent human error.…”

Section: Introductionmentioning

confidence: 99%

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Human Error Prediction Using Heart Rate Variability and Electroencephalography

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Sugaya

2022

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As human’s simple tasks are being increasingly replaced by autonomous systems and robots, it is likely that the responsibility of handling more complex tasks will be more often placed on human workers. Thus, situations in which workplace tasks change before human workers become proficient at those tasks will arise more frequently due to rapid changes in business trends. Based on this background, the importance of preventing human error will become increasingly crucial. Existing studies on human error reveal how task errors are related to heart rate variability (HRV) indexes and electroencephalograph (EEG) indexes. However, in terms of preventing human error, analysis on their relationship with conditions before human error occurs (i.e., the human pre-error state) is still insufficient. This study aims at identifying biological indexes potentially useful for the detection of high-risk psychological states. As a result of correlation analysis between the number of errors in a Stroop task and the multiple HRV and EEG indexes obtained before and during the task, significant correlations were obtained with respect to several biological indexes. Specifically, we confirmed that conditions before the task are important for predicting the human error risk in high-cognitive-load tasks while conditions both before and during tasks are important in low-cognitive-load tasks.

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