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{
"subject": "Engineering - Human Factor",
"paper_id": "1",
"paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
"question_id": "Q1",
"question": "What are the subjects’ occupational roles?",
"choices": {
"A": "Drivers",
"B": "Warehouse worker",
"C": "Electrical line workers",
"D": "Atheletes",
"E": "All of above",
"F": "None of above"
},
"answer": "C",
"metadata": {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "Occupation",
"term_explanation": "What kind of work or main activity do the people in the study do—for example, do they have regular jobs, play sports, drive vehicles, or something else?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "1",
"paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
"question_id": "Q2",
"question": "What specific task or activity are the subjects performing?",
"choices": {
"A": "Hoisting",
"B": "Lifting",
"C": "Standing",
"D": "Electrical panel work",
"E": "All of above",
"F": "None of above"
},
"answer": "E",
"metadata": {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "Primary Task",
"term_explanation": "What are the people in the study doing—for example, are they lifting something, walking, running, putting things together, or carrying out another kind of activity?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "1",
"paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
"question_id": "Q4",
"question": "What is the study context or environment in which participants perform the tasks? If the data are referenced from prior work, please indicate the source.",
"choices": {
"A": "Lab",
"B": "Field",
"C": "Computer simulated",
"D": "Mixed",
"E": "All of above",
"F": "None of above"
},
"answer": "A",
"metadata": {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "Study Environment",
"term_explanation": "Where are the people in the study doing the task—for example, is it in a lab, out in the real world, or in a mix of both places? If the data is from the reference or open data, please point this out."
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "1",
"paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
"question_id": "Q5",
"question": "What are the primary sensing modalities or measurement instruments employed in the study to capture human performance and physiological responses?",
"choices": {
"A": "Empatica E4 wristband",
"B": "EMG",
"C": "ECG",
"D": "EEG",
"E": "All of above",
"F": "None of above"
},
"answer": "A",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sensor type",
"term_explanation": "What are the primary sensing modalities or measurement instruments employed in the study (e.g.,IMU, EMG, EEG) to capture human performance and physiological responses?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "1",
"paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
"question_id": "Q6",
"question": "What is the anatomical or body placement of the sensors used in the study?",
"choices": {
"A": "Wrist",
"B": "Waist",
"C": "Chest",
"D": "Ankle",
"E": "All of above",
"F": "None of above"
},
"answer": "A",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sensor placement",
"term_explanation": "Where on the body are the sensors placed—for example, on the wrist, chest, waist, thigh, ankle, or foot?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "1",
"paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
"question_id": "Q7",
"question": "What is the sampling rate (Hz)?",
"choices": {
"A": "0-20 Hz",
"B": "20-40 Hz",
"C": "40-60 Hz",
"D": "Above 60 Hz",
"E": "All of above",
"F": "None of above"
},
"answer": "B",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sampling rate",
"term_explanation": "How often are the measurements or data points being collected during the study—for example, are they being taken many times per second, or less frequently?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "1",
"paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
"question_id": "Q8",
"question": "What is the total number of participants involved in the study?",
"choices": {
"A": "0-10",
"B": "10-20",
"C": "20-30",
"D": "> 30",
"E": "All of above",
"F": "None of above"
},
"answer": "D",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sample Size",
"term_explanation": "How many people took part in the study?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "1",
"paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
"question_id": "Q9",
"question": "How are the participants’ physical, cognitive, or perceptual states assessed or reflected in the study?",
"choices": {
"A": "Borg RPE 6-20",
"B": "Borg RPE CR 10",
"C": "PVT, RULA",
"D": "Strength",
"E": "All of above",
"F": "None of above"
},
"answer": "F",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Label",
"term_explanation": "How do the researchers check how the participants are feeling or performing during the study—for example, by asking them how hard the task feels, measuring their heart rate, tracking mental effort, or using other ways to see how their body and mind respond?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "1",
"paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
"question_id": "Q10",
"question": "What is the primary modeling objective in this study?",
"choices": {
"A": "Regression",
"B": "Clustering",
"C": "Dimension reduction",
"D": "Classification",
"E": "All of above",
"F": "None of above"
},
"answer": "D",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Machine Learning task",
"term_explanation": "What is the goal of the model in the study? For example, is it trying to sort things into groups, make predictions about numbers, or find patterns in the data?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "1",
"paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
"question_id": "Q11",
"question": "What is the data partitioning strategy used during model training, and what are the parameters?",
"choices": {
"A": "T-T split; 0.8, 0.2",
"B": "T-D-T split; 0.525, 0.175, 0.3",
"C": "K-fold; 7 fold",
"D": "K-fold; 10 fold",
"E": "All of above",
"F": "None of above"
},
"answer": "A",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Splitting strategy (tdt split or k-fold)",
"term_explanation": "How do the researchers split the data for the model to learn—which part is used to teach the model, which part is used to check if it learned correctly, and whether any is kept aside to fine-tune the model?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "1",
"paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
"question_id": "Q12",
"question": "What is the number of epochs used during model training (i.e., how many complete passes through the entire training dataset)?",
"choices": {
"A": "5",
"B": "256",
"C": "1000",
"D": "35",
"E": "All of above",
"F": "None of above"
},
"answer": "F",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Number of Epochs",
"term_explanation": "How many times does the model go through all the data while learning from it?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "1",
"paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
"question_id": "Q13",
"question": "Which performance metrics are used to assess the effectiveness of the machine learning models?",
"choices": {
"A": "Accuracy",
"B": "Precision",
"C": "Recall",
"D": "F1 score",
"E": "All of above",
"F": "None of above"
},
"answer": "E",
"metadata": {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "Performance metrics",
"term_explanation": "What measures or scores do the researchers use to see how well the model is working—for example, how often it gets things right, how accurate its predictions are, or how well it can tell different cases apart?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "1",
"paper_title": "A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor",
"question_id": "Q14",
"question": "What are the reported values for the performance metrics used to evaluate the machine learning models?",
"choices": {
"A": "Accuracy 99.7 %",
"B": ">60 % min accuracy",
"C": "p < 0.003 for any classical method compared to our method",
"D": "89.5±2.5 % F1-score",
"E": "All of above",
"F": "None of above"
},
"answer": "B",
"metadata": {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "Performance values",
"term_explanation": "What were the final results or scores that show how well the computer model performed?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "2",
"paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
"question_id": "Q1",
"question": "What are the subjects’ occupational roles?",
"choices": {
"A": "Drivers",
"B": "Warehouse worker",
"C": "Electric linemen",
"D": "Civil aviator",
"E": "All of above",
"F": "None of above"
},
"answer": "F",
"metadata": {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "Occupation",
"term_explanation": "What kind of work or main activity do the people in the study do—for example, do they have regular jobs, play sports, drive vehicles, or something else?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "2",
"paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
"question_id": "Q2",
"question": "What specific task or activity are the subjects performing?",
"choices": {
"A": "Communication",
"B": "Tracking",
"C": "System monitoring",
"D": "Resource management",
"E": "All of above",
"F": "None of above"
},
"answer": "E",
"metadata": {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "Primary Task",
"term_explanation": "What are the people in the study doing—for example, are they lifting something, walking, running, putting things together, or carrying out another kind of activity?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "2",
"paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
"question_id": "Q4",
"question": "What is the study context or environment in which participants perform the tasks? If the data are referenced from prior work, please indicate the source.",
"choices": {
"A": "Lab",
"B": "Field",
"C": "Computer simulated",
"D": "Mixed",
"E": "All of above",
"F": "None of above"
},
"answer": "A",
"metadata": {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "Study Environment",
"term_explanation": "Where are the people in the study doing the task—for example, is it in a lab, out in the real world, or in a mix of both places? If the data is from the reference or open data, please point this out."
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "2",
"paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
"question_id": "Q5",
"question": "What are the primary sensing modalities or measurement instruments employed in the study to capture human performance and physiological responses?",
"choices": {
"A": "IMU",
"B": "EMG",
"C": "ECG",
"D": "EEG",
"E": "All of above",
"F": "None of above"
},
"answer": "D",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sensor type",
"term_explanation": "What are the primary sensing modalities or measurement instruments employed in the study (e.g.,IMU, EMG, EEG) to capture human performance and physiological responses?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "2",
"paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
"question_id": "Q6",
"question": "What is the anatomical or body placement of the sensors used in the study?",
"choices": {
"A": "Wrist",
"B": "Waist",
"C": "Head",
"D": "Ankle",
"E": "All of above",
"F": "None of above"
},
"answer": "C",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sensor placement",
"term_explanation": "Where on the body are the sensors placed—for example, on the wrist, chest, waist, thigh, ankle, or foot?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "2",
"paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
"question_id": "Q7",
"question": "What is the sampling rate (Hz)?",
"choices": {
"A": "0-20 Hz",
"B": "20-40 Hz",
"C": "40-60 Hz",
"D": "Above 60 Hz",
"E": "All of above",
"F": "None of above"
},
"answer": "D",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sampling rate",
"term_explanation": "How often are the measurements or data points being collected during the study—for example, are they being taken many times per second, or less frequently?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "2",
"paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
"question_id": "Q8",
"question": "What is the total number of participants involved in the study?",
"choices": {
"A": "0-10",
"B": "10-20",
"C": "20-30",
"D": "> 30",
"E": "All of above",
"F": "None of above"
},
"answer": "B",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sample Size",
"term_explanation": "How many people took part in the study?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "2",
"paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
"question_id": "Q9",
"question": "How are the participants’ physical, cognitive, or perceptual states assessed or reflected in the study?",
"choices": {
"A": "Borg RPE 6-20",
"B": "Borg RPE CR 10",
"C": "PVT, RULA",
"D": "Stanford Sleepiness Scale",
"E": "All of above",
"F": "None of above"
},
"answer": "D",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Label",
"term_explanation": "How do the researchers check how the participants are feeling or performing during the study—for example, by asking them how hard the task feels, measuring their heart rate, tracking mental effort, or using other ways to see how their body and mind respond?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "2",
"paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
"question_id": "Q10",
"question": "What is the primary modeling objective in this study?",
"choices": {
"A": "Regression",
"B": "Clustering",
"C": "Dimension reduction",
"D": "Classification",
"E": "All of above",
"F": "None of above"
},
"answer": "D",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Machine Learning task",
"term_explanation": "What is the goal of the model in the study? For example, is it trying to sort things into groups, make predictions about numbers, or find patterns in the data?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "2",
"paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
"question_id": "Q11",
"question": "What is the data partitioning strategy used during model training, and what are the parameters?",
"choices": {
"A": "T-T split; 0.8, 0.2",
"B": "T-D-T split; 0.8, 0.1, 0.1",
"C": "K-fold; 7 fold",
"D": "K-fold; 10 fold",
"E": "All of above",
"F": "None of above"
},
"answer": "A",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Splitting strategy (tdt split or k-fold)",
"term_explanation": "How do the researchers split the data for the model to learn—which part is used to teach the model, which part is used to check if it learned correctly, and whether any is kept aside to fine-tune the model?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "2",
"paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
"question_id": "Q12",
"question": "What is the number of epochs used during model training (i.e., how many complete passes through the entire training dataset)?",
"choices": {
"A": "50",
"B": "300",
"C": "1000",
"D": "3500",
"E": "All of above",
"F": "None of above"
},
"answer": "C",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Number of Epochs",
"term_explanation": "How many times does the model go through all the data while learning from it?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "2",
"paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
"question_id": "Q13",
"question": "Which performance metrics are used to assess the effectiveness of the machine learning models?",
"choices": {
"A": "Accuracy",
"B": "Precision",
"C": "Recall",
"D": "F1 score",
"E": "All of above",
"F": "None of above"
},
"answer": "E",
"metadata": {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "Performance metrics",
"term_explanation": "What measures or scores do the researchers use to see how well the model is working—for example, how often it gets things right, how accurate its predictions are, or how well it can tell different cases apart?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "2",
"paper_title": "Assessing human situation awareness reliability considering fatigue and mood using EEG data: A Bayesian neural network-Bayesian network approach",
"question_id": "Q14",
"question": "What are the reported values for the performance metrics used to evaluate the machine learning models?",
"choices": {
"A": "Detection accuracy of 99.7 %",
"B": "98 % accuracy, >97 % precision, >97 % recall and >98 % F1 score",
"C": "p < 0.003",
"D": "A strong correlation",
"E": "All of above",
"F": "None of above"
},
"answer": "B",
"metadata": {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "Performance values",
"term_explanation": "What were the final results or scores that show how well the computer model performed?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "3",
"paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
"question_id": "Q1",
"question": "What are the subjects’ occupational roles?",
"choices": {
"A": "Drivers",
"B": "Warehouse worker",
"C": "Electric linemen",
"D": "Atheletes",
"E": "All of above",
"F": "None of above"
},
"answer": "A",
"metadata": {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "Occupation",
"term_explanation": "What kind of work or main activity do the people in the study do—for example, do they have regular jobs, play sports, drive vehicles, or something else?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "3",
"paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
"question_id": "Q2",
"question": "What specific task or activity are the subjects performing?",
"choices": {
"A": "Driving",
"B": "Lifting",
"C": "Standing",
"D": "Panel work",
"E": "All of above",
"F": "None of above"
},
"answer": "A",
"metadata": {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "Primary Task",
"term_explanation": "What are the people in the study doing—for example, are they lifting something, walking, running, putting things together, or carrying out another kind of activity?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "3",
"paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
"question_id": "Q4",
"question": "What is the study context or environment in which participants perform the tasks? If the data are referenced from prior work, please indicate the source.",
"choices": {
"A": "Lab",
"B": "Field",
"C": "Computer simulated",
"D": "Mixed",
"E": "All of above",
"F": "None of above"
},
"answer": "A",
"metadata": {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "Study Environment",
"term_explanation": "Where are the people in the study doing the task—for example, is it in a lab, out in the real world, or in a mix of both places? If the data is from the reference or open data, please point this out."
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "3",
"paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
"question_id": "Q5",
"question": "What are the primary sensing modalities or measurement instruments employed in the study to capture human performance and physiological responses?",
"choices": {
"A": "IMU",
"B": "EEG",
"C": "ECG",
"D": "IMU and EEG",
"E": "All of above",
"F": "None of above"
},
"answer": "D",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sensor type",
"term_explanation": "What are the primary sensing modalities or measurement instruments employed in the study (e.g.,IMU, EMG, EEG) to capture human performance and physiological responses?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "3",
"paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
"question_id": "Q6",
"question": "What is the anatomical or body placement of the sensors used in the study?",
"choices": {
"A": "Head",
"B": "Neck",
"C": "Sternum",
"D": "Head, neck and sternum",
"E": "All of above",
"F": "None of above"
},
"answer": "D",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sensor placement",
"term_explanation": "Where on the body are the sensors placed—for example, on the wrist, chest, waist, thigh, ankle, or foot?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "3",
"paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
"question_id": "Q7",
"question": "What is the sampling rate (Hz)?",
"choices": {
"A": "0-20 Hz",
"B": "20-40 Hz",
"C": "40-60 Hz",
"D": "Above 60 Hz",
"E": "All of above",
"F": "None of above"
},
"answer": "C",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sampling rate",
"term_explanation": "How often are the measurements or data points being collected during the study—for example, are they being taken many times per second, or less frequently?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "3",
"paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
"question_id": "Q8",
"question": "What is the total number of participants involved in the study?",
"choices": {
"A": "0-10",
"B": "10-20",
"C": "20-30",
"D": "> 30",
"E": "All of above",
"F": "None of above"
},
"answer": "C",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sample Size",
"term_explanation": "How many people took part in the study?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "3",
"paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
"question_id": "Q9",
"question": "How are the participants’ physical, cognitive, or perceptual states assessed or reflected in the study?",
"choices": {
"A": "Borg RPE 6-20",
"B": "Borg RPE CR 10",
"C": "PVT, RULA",
"D": "Strength",
"E": "All of above",
"F": "None of above"
},
"answer": "F",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Label",
"term_explanation": "How do the researchers check how the participants are feeling or performing during the study—for example, by asking them how hard the task feels, measuring their heart rate, tracking mental effort, or using other ways to see how their body and mind respond?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "3",
"paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
"question_id": "Q10",
"question": "What is the primary modeling objective in this study?",
"choices": {
"A": "Regression",
"B": "Clustering",
"C": "Clustering and classification",
"D": "Classification",
"E": "All of above",
"F": "None of above"
},
"answer": "C",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Machine Learning task",
"term_explanation": "What is the goal of the model in the study? For example, is it trying to sort things into groups, make predictions about numbers, or find patterns in the data?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "3",
"paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
"question_id": "Q11",
"question": "What is the data partitioning strategy used during model training, and what are the parameters?",
"choices": {
"A": "T-T split; 20, 2",
"B": "T-D-T split; 0.8, 0.1, 0.1",
"C": "K-fold; 5 fold",
"D": "K-fold; 10 fold",
"E": "All of above",
"F": "None of above"
},
"answer": "A",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Splitting strategy (tdt split or k-fold)",
"term_explanation": "How do the researchers split the data for the model to learn—which part is used to teach the model, which part is used to check if it learned correctly, and whether any is kept aside to fine-tune the model?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "3",
"paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
"question_id": "Q12",
"question": "What is the number of epochs used during model training (i.e., how many complete passes through the entire training dataset)?",
"choices": {
"A": "50",
"B": "300",
"C": "1000",
"D": "3500",
"E": "All of above",
"F": "None of above"
},
"answer": "F",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Number of Epochs",
"term_explanation": "How many times does the model go through all the data while learning from it?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "3",
"paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
"question_id": "Q13",
"question": "Which performance metrics are used to assess the effectiveness of the machine learning models?",
"choices": {
"A": "Accuracy",
"B": "Precision",
"C": "Sensitivity",
"D": "F1 score",
"E": "All of above",
"F": "None of above"
},
"answer": "E",
"metadata": {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "Performance metrics",
"term_explanation": "What measures or scores do the researchers use to see how well the model is working—for example, how often it gets things right, how accurate its predictions are, or how well it can tell different cases apart?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "3",
"paper_title": "Automatic driver cognitive fatigue detection based on upper body posture variations",
"question_id": "Q14",
"question": "What are the reported values for the performance metrics used to evaluate the machine learning models?",
"choices": {
"A": "Detection accuracy of 99.7 %",
"B": "98 % accuracy, >97 % precision, >97 % recall and >98 % F1 score",
"C": ">79 % precision, >70 % sensitivity and >74 % F1 score",
"D": "A strong correlation",
"E": "All of above",
"F": "None of above"
},
"answer": "C",
"metadata": {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "Performance values",
"term_explanation": "What were the final results or scores that show how well the computer model performed?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "4",
"paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
"question_id": "Q1",
"question": "What are the subjects’ occupational roles?",
"choices": {
"A": "Drivers",
"B": "Warehouse worker",
"C": "Electric linemen",
"D": "Atheletes",
"E": "All of above",
"F": "None of above"
},
"answer": "F",
"metadata": {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "Occupation",
"term_explanation": "What kind of work or main activity do the people in the study do—for example, do they have regular jobs, play sports, drive vehicles, or something else?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "4",
"paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
"question_id": "Q2",
"question": "What specific task or activity are the subjects performing?",
"choices": {
"A": "Assembly",
"B": "Lifting",
"C": "Walking with load",
"D": "Walking without load",
"E": "All of above",
"F": "None of above"
},
"answer": "E",
"metadata": {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "Primary Task",
"term_explanation": "What are the people in the study doing—for example, are they lifting something, walking, running, putting things together, or carrying out another kind of activity?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "4",
"paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
"question_id": "Q4",
"question": "What is the study context or environment in which participants perform the tasks? If the data are referenced from prior work, please indicate the source.",
"choices": {
"A": "Lab",
"B": "Field",
"C": "Computer simulated",
"D": "Reference or open data",
"E": "All of above",
"F": "None of above"
},
"answer": "D",
"metadata": {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "Study Environment",
"term_explanation": "Where are the people in the study doing the task—for example, is it in a lab, out in the real world, or in a mix of both places? If the data is from the reference or open data, please point this out."
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "4",
"paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
"question_id": "Q5",
"question": "What are the primary sensing modalities or measurement instruments employed in the study to capture human performance and physiological responses?",
"choices": {
"A": "GSR",
"B": "HR",
"C": "EMG",
"D": "Infrared camera",
"E": "All of above",
"F": "None of above"
},
"answer": "E",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sensor type",
"term_explanation": "What are the primary sensing modalities or measurement instruments employed in the study (e.g.,IMU, EMG, EEG) to capture human performance and physiological responses?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "4",
"paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
"question_id": "Q6",
"question": "What is the anatomical or body placement of the sensors used in the study?",
"choices": {
"A": "Wrist",
"B": "Waist",
"C": "Head",
"D": "Ankle",
"E": "All of above",
"F": "None of above"
},
"answer": "F",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sensor placement",
"term_explanation": "Where on the body are the sensors placed—for example, on the wrist, chest, waist, thigh, ankle, or foot?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "4",
"paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
"question_id": "Q7",
"question": "What is the sampling rate (Hz)?",
"choices": {
"A": "5 Hz",
"B": "10 Hz",
"C": "15 Hz",
"D": "20 Hz",
"E": "All of above",
"F": "None of above"
},
"answer": "F",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sampling rate",
"term_explanation": "How often are the measurements or data points being collected during the study—for example, are they being taken many times per second, or less frequently?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "4",
"paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
"question_id": "Q8",
"question": "What is the total number of participants involved in the study?",
"choices": {
"A": "10, 15",
"B": "20, 10",
"C": "30, 15",
"D": "24, 11",
"E": "All of above",
"F": "None of above"
},
"answer": "D",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sample Size",
"term_explanation": "How many people took part in the study?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "4",
"paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
"question_id": "Q9",
"question": "How are the participants’ physical, cognitive, or perceptual states assessed or reflected in the study?",
"choices": {
"A": "Borg RPE 6-20",
"B": "Borg RPE CR 10",
"C": "PVT, RULA",
"D": "Strength",
"E": "All of above",
"F": "None of above"
},
"answer": "F",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Label",
"term_explanation": "How do the researchers check how the participants are feeling or performing during the study—for example, by asking them how hard the task feels, measuring their heart rate, tracking mental effort, or using other ways to see how their body and mind respond?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "4",
"paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
"question_id": "Q10",
"question": "What is the primary modeling objective in this study?",
"choices": {
"A": "Regression",
"B": "Clustering",
"C": "Dimension reduction",
"D": "Classification",
"E": "All of above",
"F": "None of above"
},
"answer": "D",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Machine Learning task",
"term_explanation": "What is the goal of the model in the study? For example, is it trying to sort things into groups, make predictions about numbers, or find patterns in the data?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "4",
"paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
"question_id": "Q11",
"question": "What is the data partitioning strategy used during model training, and what are the parameters?",
"choices": {
"A": "T-T split; 0.7, 0.3",
"B": "T-T split; 0.8, 0.2",
"C": "K-fold; 5 fold",
"D": "K-fold; 10 fold",
"E": "All of above",
"F": "None of above"
},
"answer": "B",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Splitting strategy (tdt split or k-fold)",
"term_explanation": "How do the researchers split the data for the model to learn—which part is used to teach the model, which part is used to check if it learned correctly, and whether any is kept aside to fine-tune the model?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "4",
"paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
"question_id": "Q12",
"question": "What is the number of epochs used during model training (i.e., how many complete passes through the entire training dataset)?",
"choices": {
"A": "50",
"B": "100",
"C": "600",
"D": "1700",
"E": "All of above",
"F": "None of above"
},
"answer": "B",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Number of Epochs",
"term_explanation": "How many times does the model go through all the data while learning from it?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "4",
"paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
"question_id": "Q13",
"question": "Which performance metrics are used to assess the effectiveness of the machine learning models?",
"choices": {
"A": "Accuracy",
"B": "Precision",
"C": "Recall",
"D": "Predicted, recall and F1-score",
"E": "All of above",
"F": "None of above"
},
"answer": "D",
"metadata": {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "Performance metrics",
"term_explanation": "What measures or scores do the researchers use to see how well the model is working—for example, how often it gets things right, how accurate its predictions are, or how well it can tell different cases apart?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "4",
"paper_title": "Enhancing Data Privacy in Human Factors Studies with Federated Learning",
"question_id": "Q14",
"question": "What are the reported values for the performance metrics used to evaluate the machine learning models?",
"choices": {
"A": "Detection accuracy of 99.7 %",
"B": "98 % accuracy, >97 % precision, >97 % recall and >98 % F1 score",
"C": ">70 % precision, >75 % recall and >72 % F1 score",
"D": "A strong correlation",
"E": "All of above",
"F": "None of above"
},
"answer": "C",
"metadata": {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "Performance values",
"term_explanation": "What were the final results or scores that show how well the computer model performed?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "5",
"paper_title": "Worker’s physical fatigue classification using neural networks",
"question_id": "Q1",
"question": "What are the subjects’ occupational roles?",
"choices": {
"A": "Drivers",
"B": "Warehouse worker",
"C": "Electric linemen",
"D": "Atheletes",
"E": "All of above",
"F": "None of above"
},
"answer": "B",
"metadata": {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "Occupation",
"term_explanation": "What kind of work or main activity do the people in the study do—for example, do they have regular jobs, play sports, drive vehicles, or something else?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "5",
"paper_title": "Worker’s physical fatigue classification using neural networks",
"question_id": "Q2",
"question": "What specific task or activity are the subjects performing?",
"choices": {
"A": "Walking",
"B": "Bending",
"C": "Standing",
"D": "Assembly",
"E": "All of above",
"F": "None of above"
},
"answer": "E",
"metadata": {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "Primary Task",
"term_explanation": "What are the people in the study doing—for example, are they lifting something, walking, running, putting things together, or carrying out another kind of activity?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "5",
"paper_title": "Worker’s physical fatigue classification using neural networks",
"question_id": "Q4",
"question": "What is the study context or environment in which participants perform the tasks? If the data are referenced from prior work, please indicate the source.",
"choices": {
"A": "Lab",
"B": "Field",
"C": "Computer simulated",
"D": "Reference or open data",
"E": "All of above",
"F": "None of above"
},
"answer": "D",
"metadata": {
"Task-oriented Category": "Study subject & experimental setup",
"question_key_term": "Study Environment",
"term_explanation": "Where are the people in the study doing the task—for example, is it in a lab, out in the real world, or in a mix of both places? If the data is from the reference or open data, please point this out."
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "5",
"paper_title": "Worker’s physical fatigue classification using neural networks",
"question_id": "Q5",
"question": "What are the primary sensing modalities or measurement instruments employed in the study to capture human performance and physiological responses?",
"choices": {
"A": "IMU",
"B": "EMG",
"C": "ECG",
"D": "EEG",
"E": "All of above",
"F": "None of above"
},
"answer": "A",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sensor type",
"term_explanation": "What are the primary sensing modalities or measurement instruments employed in the study (e.g.,IMU, EMG, EEG) to capture human performance and physiological responses?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "5",
"paper_title": "Worker’s physical fatigue classification using neural networks",
"question_id": "Q6",
"question": "What is the anatomical or body placement of the sensors used in the study?",
"choices": {
"A": "Wrist",
"B": "Torso",
"C": "Hip",
"D": "Ankle",
"E": "All of above",
"F": "None of above"
},
"answer": "E",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sensor placement",
"term_explanation": "Where on the body are the sensors placed—for example, on the wrist, chest, waist, thigh, ankle, or foot?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "5",
"paper_title": "Worker’s physical fatigue classification using neural networks",
"question_id": "Q7",
"question": "What is the sampling rate (Hz)?",
"choices": {
"A": "0-20 Hz",
"B": "20-40 Hz",
"C": "40-60 Hz",
"D": "Above 60 Hz",
"E": "All of above",
"F": "None of above"
},
"answer": "C",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sampling rate",
"term_explanation": "How often are the measurements or data points being collected during the study—for example, are they being taken many times per second, or less frequently?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "5",
"paper_title": "Worker’s physical fatigue classification using neural networks",
"question_id": "Q8",
"question": "What is the total number of participants involved in the study?",
"choices": {
"A": "0-10",
"B": "10-20",
"C": "20-30",
"D": "> 30",
"E": "All of above",
"F": "None of above"
},
"answer": "A",
"metadata": {
"Task-oriented Category": "Data characteristics & collection",
"question_key_term": "Sample Size",
"term_explanation": "How many people took part in the study?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "5",
"paper_title": "Worker’s physical fatigue classification using neural networks",
"question_id": "Q9",
"question": "How are the participants’ physical, cognitive, or perceptual states assessed or reflected in the study?",
"choices": {
"A": "Borg RPE",
"B": "Stanford Sleepiness Scale",
"C": "PVT, RULA",
"D": "Strength",
"E": "All of above",
"F": "None of above"
},
"answer": "A",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Label",
"term_explanation": "How do the researchers check how the participants are feeling or performing during the study—for example, by asking them how hard the task feels, measuring their heart rate, tracking mental effort, or using other ways to see how their body and mind respond?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "5",
"paper_title": "Worker’s physical fatigue classification using neural networks",
"question_id": "Q10",
"question": "What is the primary modeling objective in this study?",
"choices": {
"A": "Regression",
"B": "Classification",
"C": "Dimension reduction",
"D": "Classification and regression",
"E": "All of above",
"F": "None of above"
},
"answer": "B",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Machine Learning task",
"term_explanation": "What is the goal of the model in the study? For example, is it trying to sort things into groups, make predictions about numbers, or find patterns in the data?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "5",
"paper_title": "Worker’s physical fatigue classification using neural networks",
"question_id": "Q11",
"question": "What is the data partitioning strategy used during model training, and what are the parameters?",
"choices": {
"A": "T-T split; 0.7, 0.3",
"B": "T-T split; 0.8, 0.2",
"C": "K-fold; 5 fold",
"D": "K-fold; 10 fold",
"E": "All of above",
"F": "None of above"
},
"answer": "B",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Splitting strategy (tdt split or k-fold)",
"term_explanation": "How do the researchers split the data for the model to learn—which part is used to teach the model, which part is used to check if it learned correctly, and whether any is kept aside to fine-tune the model?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "5",
"paper_title": "Worker’s physical fatigue classification using neural networks",
"question_id": "Q12",
"question": "What is the number of epochs used during model training (i.e., how many complete passes through the entire training dataset)?",
"choices": {
"A": "100",
"B": "200",
"C": "300",
"D": "500 and 1000",
"E": "All of above",
"F": "None of above"
},
"answer": "D",
"metadata": {
"Task-oriented Category": "Technical approach & details",
"question_key_term": "Number of Epochs",
"term_explanation": "How many times does the model go through all the data while learning from it?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "5",
"paper_title": "Worker’s physical fatigue classification using neural networks",
"question_id": "Q13",
"question": "Which performance metrics are used to assess the effectiveness of the machine learning models?",
"choices": {
"A": "Accuracy",
"B": "R squre",
"C": "Recall",
"D": "MAE",
"E": "All of above",
"F": "None of above"
},
"answer": "A",
"metadata": {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "Performance metrics",
"term_explanation": "What measures or scores do the researchers use to see how well the model is working—for example, how often it gets things right, how accurate its predictions are, or how well it can tell different cases apart?"
}
},
{
"subject": "Engineering - Human Factor",
"paper_id": "5",
"paper_title": "Worker’s physical fatigue classification using neural networks",
"question_id": "Q14",
"question": "What are the reported values for the performance metrics used to evaluate the machine learning models?",
"choices": {
"A": "Acc, 0.999; R square 0.98",
"B": ">80 % accuracy",
"C": "R square 0.98",
"D": "R square 0.993",
"E": "All of above",
"F": "None of above"
},
"answer": "B",
"metadata": {
"Task-oriented Category": "Conclusions & results",
"question_key_term": "Performance values",
"term_explanation": "What were the final results or scores that show how well the computer model performed?"
}
}
]
|