https://mechta.ub.ac.id/
DOI: 10.21776/MECHTA.2024.005.01.11
108
CARDIAC BIOMETRICS AND PERCEIVED WORKLOAD REGRESSION ANALYSIS USING RANDOM FOREST REGRESSOR IN COGNITIVE MANUFACTURING TASKS
Afifah Harmayanti 1) ✉, Ishardita Pambudi Tama 2), Femiana Gapsari 1), Zuardin Akbar 3), Hans Juliano 4)
1) Mechanical Engineering Deptartment Universitas Brawijaya
Malang, Indonesia
2) Industrial Engineering Department Universitas Brawijaya
Malang, Indonesia [email protected]
3) Institute for Computational Design and Construction
University of Stuttgart Stuttgart, Germany
4) Department of Electrical Engineering and Computer Science
National Yang Ming Chiao Tung University Hsinchu, Taiwan
Abstract
Workload is crucial in managing and maintaining good performance of human resources and allocations. In an advanced manufacturing industry, human job functions had shifted to cognitive tasks. Thus, cognitive workload evaluation should be used to monitor worker’s workload in optimal condition. Most common tool of cognitive workload tools are perceived measurement, like NASA – TLX questionnaire. Despite of its sensitivity to capture workload felt by the workers, this subjective measurement was prone to bias.
Objective measurement utilizing biometrics data of the human body during working state was useful to eliminate bias. Cardiac biometrics were one of the many that were closely related to mental activity changes. The objective of this study was to understand the relationship of cardiac biometrics to perceived workload as an indicator of cognitive workload analysis. The study utilized four biometrics, heart rate, HRV low frequency power, total frequency power and ratio of low and high frequency power, were used to analyzed a one hour long cognitive based study case. The study case was designed in a manufacturing planning context referring to manufacturing aptitude tests, to induce cognition process on 30 participants. The biometrics and NASA – TLX score result of all the participants, were then calculated as effect size standardization before input into random forest regressor model to analyze relationship between cardiac biometrics and perceived workload. The result found a moderate relationship between the two (r2 = 0.576). Features importance also showed the most impactful feature to the model is the effect size of frequency power ratio. However, it is recommended to always consider evaluating multiple cardiac biometrics in workload analysis to ensure good model performance.
Keywords: Cardiac Biometrics, Machine Learning, Cognitive Workload, Manufacturing, Feature Importance.
1. INTRODUCTION
Evaluating workers’ workload is crucial in managing resources allocation and performance.
In manufacturing industry, evaluating workload can be carried out in every part through all the entire supply chain process. Human factors affect the quality of whole production process
[1]. Studies found relationship between human factors and human error [2,3] and occupational incidents [4,5]. Thus, monitoring human workload to ensure their working quality is important to maintain the quality of overall process.
The working quality of a worker in industry 4.0 is closely relate to their cognitive ability.
In this new era and the future 5.0, human will be exposed to more cognitive work rather than physical work. Cognitive based task assessment such as designing and planning, even though has been simplified using software utilization, is still closely dependable to the worker’s cognitive ability. Besides, human interaction with machine, computers and other digital interfaces could add additional load to human cognitive function. The field of cognitive ergonomic is important to maintain worker’s work quality that would affect the overall system’s performance [6,7]. Studies have been carried out to understand how human respond during interaction with technology [8–10] and virtual environments [9,11–13]. In logistic sector, understanding driver’s cognitive load could also be utilized for supply chain divisions [14]. As long as humans are involved, the study of cognitive ergonomic will always be relevant to analyze and maintain human working quality.
As the demand of cognition in manufacturing tasks are increasing, innovation of cognitive ergonomic is the need of evaluating cognitive workload is crucial. Perceived workload measurements are the most common tools to evaluate cognitive workload. Despite of its sensitivity to capture workload felt by the subject [1], subjective measurements are still prone to bias [3]. Thus, objective methods which uses physiological indicators are much preferred and need to be studied. Utilization of sensors in cognitive ergonomic to capture physiological condition are massively studied in recent years. Those sensors could detect human physiological changes as signals resulting in biometrics. Recent studies use biometrics from physiological signals such as electrodermal, cardiac and electroencephalography activities for workload analysis [10,15–19]. However, most of the studies in manufacturing sectors, were mostly carried out using physical dominated tasks such as assembling and maintenance activities [8,10]. Cognitive based tasks such as designing and planning activities were not yet to be found. Data interpretation, calculation and logical thinking in designing and planning process, utilize human cognitive function. Thus, cognitive workload in these types of tasks is also needed to be analyzed. A study found that product design could affect the quality of how the product would be manufactured and assembled [20]. Other than the nature of the tasks, personal individual factors like age, gender, lifestyle impacts and personal issues should also be taken into considerations during measurement [3]. To focus on finding the value of workload as an effect of the tasks given, the effect size method principle could be used. The effect size principle is to calculate the value of workload effect caused by the tasks by normalizing it to the value before a subject started doing the task [1]. This principle could be applied to biometrics data to measure cognitive workload during a task activity, to eliminate personal factors, that could affect physiological readings.
Regression models have developed rapidly over the years and implemented in various industries. These models have been widely used in fields like architecture [21], supply chain
[14] and manufacturing [10]. Results from these studies show high accuracy in their performances according to their respective functions. In this study, a random forest regressor
[22] analysis will be used to understand the relationship between cardiac biometrics and perceived workload as an indicator for cognitive workload analysis. The analysis will use effect size principle that is implemented to cardiac biometrics for workload analysis using a study case based on design and planning situation. The questions for study case are design to induce subjects’ data interpretation, calculation and decision – making skills. Perceived workload index of each subject is also evaluated using National Aeronautics and Space Administration Task Load Index (NASA – TLX). NASA – TLX is the gold standard of workload assessment and has been used worldwide [23,24]. Cardiac biometrics effect sizes relationship to NASA – TLX score is then analyzed using random forest regressor model.
Biometrics data are non – linear data. So, the use of machine learning algorithm is very helpful to overcome this obstacle. The R2 and partial dependence analysis from the model will explain the relationship between cardiac signals to perceived workload endure by the subjects.
2. MATERIALS AND METHODS
2.1 NASA – TLX for Perceived Workload Evaluation
There are several methods to evaluate perceived workload. One of them is the NASA – TLX developed by Hart [23]. NASA – TLX has been used in various fields including health services [25,26], public services [5], aviation [27], mining industry [28], and manufacturing industries [3,29]. Even though perceived workload is prone to subject’s bias on judging the workload they’ve experienced [3], NASA – TLX has showed a reliable result for analyzing perceived workload because of its sensitivity to humans’ perception in explaining the workload they’re experiencing [1,27]. In this study, perceived workload will be evaluated using NASA – TLX questionnaire. To prevent participants misunderstood the context of the questionnaire, pictures and short sentences are used in the questionnaire. Combination of pictures and simple sentences found to be effective in delivering information context [30,31]. The language of the questionnaire is also translated to Bahasa Indonesia, adapting to the participants’ mother tongue.
2.2 Cardiac Biometrics for Physiological Indicator of Workload
The autonomous system, that consists of sympathetic (SNS) and parasympathetic (PNS) nervous system, maintain body regulation by controlling human system activities including cardiac activity [32]. The balance of SNS and PNS effect the heart rate (HR) and heart rate variations (HRV) [33]. Influence from external factors, such as the nature of task and working environments, could affect the SNS and PNS balance of controlling cardiac activity [33–35]. SNS and PNS activities effected cardiac rhythm resulting in heart rate variations (HRV). In context of evaluating mental state changes, the frequency power of HRV is recommended to be used as biometrics. A study found that total frequency power (TP) decreased in the present of mental activity [36], while ratio of low frequency and high frequency (LF/HF) increased
[36,37]. Low frequency power (LF) also reported to be affected during mental activity. The three biometrics will be used as the indicator of cognitive workload in this study. Aside from that, heart rate (HR) will also be taken into account and recorded. The cardiac biometrics were recorded using polar H10 heart monitor strap connected to Elite HRV applications on a smartphone (Figure 1). Polar H10 heart monitor was previously researched to have the best performance among its competitors regarding its accuracy [33]. Then, biometrics data were extracted and analyzed using Kubios software to obtain HRV TP, LF, LF/HF Ratio and HR metrics.
Figure 1. Procedure of data extraction, database preparation and regression analysis
2.3 Effect Size Calculation for Cardiac Biometrics
Previous researchers, used raw biometrics data to analyze cognitive workload. However, physiological condition of a human being is depandable to its personal traits such as body mass, age, gender, lifestyle that could affect their health condition, etc. Participants of this research also possibily had initial mental load which is irrelevant to the study case their doing.
Therefore, pre – condition of the subject before starting the case should also be considered.
Yung proposed an effect size approach to represent the impact of workload to cardiac biometrics [1]. The effect size equation, adapted from Yung’s approach [1] is as shown in Equation 1. Aside from considering personal traits of each subject, standardizing the dimension of the metrics would help to analyze and compare them regarding to the subject’s workload.
Effect Sizei = (Meani, pre – Meani, on task) / (SDi, pre) ; i = TP, LF, Ratio LF/HF, HR (1)
2.4 Participant Recruitment
In this study, 30 participants were recruited among undergraduate engineering students from University of Brawijaya, based on their health condition. History of cardiac problems and stroke were not allowed to participate. Cardiac problems could affect the readings of HR and HRV [37,38]. While stroke history had an impact to patient’s cognitive function and abilities
[39,40]. From 31 students interested to participate in the trial, 1 was eliminated due to their health condition.
2.5 Case Study Preparation
To induce mental activity, a case study is designed based on cognition principles, using manufacturing contexts. In this study, aptitude test are made as references. Aptitude tests are designed to evaluate cognitive and reasoning abilities [41]. Abstract, quantitative, verbal, data verification and spatial are among the areas that are measured as cognitive abilities [41]. Based on that references, three sets of mathematical and logical analytic questions were prepared.
Mathematical questions will stimulate the cognitive process in understanding the mathematical objects and concept of the given question [42]. While logical reasoning will
induce the participant to use context – dependent and context – specific thinking [43]. Both mathematical and logical reasoning will stimulate cognition process and caused mental state changes that could be analysed in this study.
2.6 Trial Procedure
The trial is carried out in a room without any controlled condition. It was setup as in a condition of a classroom, with an average of 101 lux for the lighting and noise 43.7 dB.
Sessions were carried out for 15 days, with 2 persons per day, on 14.00 until 17.00 Indoesian Western Time. Participants were first asked to strap on the Polar H10 strap on their chest as shown in Figure 2. Pre – data were first recorded for 15 minutes before the study case was started. Participants were then given 1 hour long to finish the given cases. Biometrics data were recorded continously during the 1 hour session. After finishing the cases, participants were then asked to answer NASA – TLX quesionnaire. As an appreciation, participants were given appropriate fees. After finishing data gathering, data were analyzed using Kubios and obtained HR, TP, LF, and LF/HF Ratio. Effect sizes of each metrics were calculated using Equation 1. All effect sizes and NASA – TLX score results were compiled into one worksheet and integrated into random forest regressor algorithm for regression analysis.
Figure 1. Position of Polar H10 (on the chest)
3. RESULTS AND DISCUSSION
The statistical result of the cardiac biometrics data of effect size LF (EFLF), effect size TP (EF TP), effect size LF/HF ratio (EF Ratio) and effect size HR (EF HR) as well as NASA – TLX score could be seen in Table 1.
Table 1. Statistical results of cardiac biometrics effect sizes and NASA – TLX score of 30 participants
Items EF
LF
EF TP
EF Ratio
EF HR
NASA-TLX Score
Mean 0.34 0.55 1.00 1.41 50.51
Standard Deviation 1.49 3.37 2.12 5.70 11.37
Min. Value -2.12 -5.99 -2.11 -9.07 33.33
Max. Value 4.10 14.97 8.79 16.91 82.00
EF LF obtained ranges from -2.12 to 4.10 with mean value 0.34 and standard deviation 1.49, showing moderate variability of low frequency power changes. EF TP had a wider range, -5.99 to 14.97 with mean 0.55 and standard deviation 3.37, showing a higher variability of total frequency power changes. EF Ratio had a range of -2.11 to 8.79, mean value 1.00 and standard deviation 2.12, showing variability of changes which is higher than low frequency power but lower than total frequency power. While EF HR had the widest range of -9.07 to 16.91, mean value 1.41 and standard deviation 5.70, showing the highest variability of all effect sizes. Statistically, TP and Ratio of LF/HR showed the most changes during the tasks. Meanwhile, NASA – TLX score ranges from 33.33 to 82.00 with mean 50.51 and standard deviation 11.37. Compared to the midpoint of cognitive activities world – wide by Grier [24] which was 46.00, the mean of this case was slightly higher but still under the 75% percentile, which is 54.66. Meanwhile, the lowest score in this study was 33.33, lower than the 25% in Grier’s study, which is 38.00 [24]. As for the maximum score here was 82.00. higher than the maximum score in Grier’s study, which is 64.90 [24]. In the comparation, the number of cognitive activities papers analyzed by Grier was 31 papers [24], which is not far from the number of participants in this study. However, the range of score variability seems higher in this study compare to Grier’s, which is also supported by the 11.37 standard deviation value of NASA – TLX score (Table 1). Overall, the characteristics of the data obtained from HR and HRV biometrics shows non – linearity.
As biometrics data are non – linear data, linear statistical analysis might not be relevant to analyze the relationship between cardiac biometrics and NASA – TLX score as representation of cognitive workload. Machine learning in the other hand, is compatible to handle non – linear data. To evaluate the relationship between the four biometrics and NASA – TLX score, random forest regressor could be used using the parameter as shown in Table 2. Since the total dataset gathered are 30, with test size 0.2, we obtained 6 subsets out of 30 for regression testing performance. Meanwhile, the remaining 24 subsets were used as training data. The 6 subsets for regression testing performance could be seen in Table 3 in sub section 4.1, along with its predicted value.
Table 2. Hyperparameters for the random forest regressor model
Hyperparamaters Value
Test_size 0.40
Train_Test_Random state 45
Regressor Estimators 100
Regressor_Random state 40
3.1 Regressor Performance Analysis
By doing regressor analysis using random forest regressor model, we obtained a moderate r2 value 0.576 and mean absolute error (MAE) 4.557. The graph and actual against predicted value of NASA – TLX score using all four features were presented in Table 3 and Figure 3.
Table 3. Data of test subsets and predicted value result by the model
Features (x_pred) NASA-TLX Score
EF LF
EF TP
EF Ratio
EF HR
Actual Value (y_test)
Predicted (y_pred)
0.339 0.169 -0.248 0.387 54.66 48.25
-0.752 -0.813 0.519 0.203 52.00 55.99
-0.133 -0.379 2.887 0.741 54.00 54.31
2.950 5.150 -0.947 -1.029 33.33 39.17
0.709 1.001 0.987 2.787 48.00 47.45
1.877 0.573 0.943 -1.324 54.00 47.76
-1.157 -1.378 3.328 4.066 49.33 50.86
-0.310 1.354 -0.822 15.020 56.00 48.26
2.441 3.538 -00781 -4.857 37.80 38.87
1.932 14.966 3.014 0.126 35.67 42.75
-0.512 -1.092 -0.397 2.435 64.33 52.33
1.171 1.364 0.326 -9.075 48.33 46.40
Figure 3. The predicted and actual value of NASA -TLX score (with error line) using random forest regressor
The r2 = 0.576 shows a moderate relationship between the four features to the NASA – TLX score, meaning combination of EF LF, EF TP, EF Ratio and EF HR explained 57.6%
variability of the NASA – TLX score. In other words, those features 57.6% had an impact to NASA – TLX score. Figure 3 shows the position of actual vs. predicted result. 5 out of 12 predictions were close into the error line, explained a small prediction error. However, 7 of them were far quite far from error line. The model predicted lower value for actual scores above 54.00, while for actual scores under 37.00 were predicted higher. As explained in Table
1, the mean value of NASA – TLX scores from 30 participants was 50.51 with standard deviation 11.37. This explained the adequate NASA – TLX sample obtained for the random forest regressor analysis in this study was range between 50.51 +/- 11.37. The model possibly able to get enough training data within those subsets. A larger dataset could help to analyse more on this matter. However, r2 = 0.576 is adequate enough to show the relationship between the four features to NASA – TLX score.
3.2 Cardiac Biometrics Analysis in Cognitive Workload Measurement
Feature importance analysis showed EF Ratio, with the value 0.42, as the most important feature among all four. Followed by EF TP with the value 0.27, EF LF with the value 0.16 and EF HR 0.13. Feature importance in random forest is derived from impurity measurement.
Impurity represents how diversed the target variables within a node. In random forest regressor, impurity measurement based on mean squared error. The highest error happened when EF Ratio were altered, followed by EF TP, EF LF and EF HR respectively. This result supported previous argument of LF/HF ratio and total frequency power that was affected by mental state changes [36]. Meanwhile, the EF LF was not significant enough as an important features to the model. EF HR was also found not giving significant affect to the random forest regressor.
Figure 4. Feature importance for the analysed random forest regressor model
Further understanding of cardiac features towards NASA – TLX score as a representation of cognitive workload, could also be analyzed using partial dependence analysis. Figure 5.a showed partial dependence plot EF LF. As EF LF tended to decrease, NASA – TLX score increased. The plot also showed negative values of EF LF were found in above 50.00 results. Negative value indicated a decreased value of LF power. A statement from previous study saying the increased ratio of LF/HF is not necessarily caused by an increased activity of SNS which is indicated by increased LF power [34]. Instead, the increased ratio possibly caused by significant decreased of SNS indicated by decreased HF power [34]. As for scores under 50.00 showed positive value of EF LF, which indicated an increment of LF power. The same pattern was also found in Figure 5.b showing relationship between TP power and NASA – TLX score. EF TP values were found positive when resulting in NASA – TLX score under 50.00.
(a) (b) Figure 5. Partial dependence plot of (a) EF LF and (b) EF TP to NASA – TLX scores
Figure 6. Partial dependence plot of EF Ratio to NASA – TLX scores
During high mental activity, the brain activity stimulates SNS activation and caused increased activity of LF power. However, human body is a complex system and designed to maintain its homeostatic state. The increased of activity could produced certain hormone like adenosine [44] that slows down cardiac activity, which also will decrease LF power.
Since the metrics used in this study were in effect sizes form, which used mean values of each metrics in the calculation, the result of effect sizes were actually showing accumulative condition of the subjects in an hour long working state, not per second differences. Negative effect size result of LF power obtained, showed the average condition of LF activity in an hour long, which was decreasing compare to pre – working state. There could be an increament of LF power in the beginning of the cognitive task as stated in previous references [36], but slowly decreased along the 1 – hour working state, resulted a final result of lower LF power compare to pre – task condition.This could happened due to adenosine accumulation, which caused the LF power to decrease. Meanwhile, the HF power, that is controlled by PNS activity that had significantly drop during the start of the working, was not high enough to balance the SNS activity. Thus resulting a decrement of TP and LF but increment of EF Ratio as shown in Figure 6.
The simultant activity of SNS and PNS during any acitivities, specifically cognitive activity, were found to collaborate with each other. Thus, in analysing cognitive workload, all biometrics should be taken into consideration, despite of the model having one or two highest feature importances. This is because, SNS and PNS never worked alone, and always worked together. The flat line in EF TP on NASA – TLX score around 52.00, showed this feature did not contribute much in that area. However, at the same area, EF LF and EF Ratio
showed fluctuative values, showing the contribution of the two features in predicting the outcomes. This explained that in this model, each features contributed most in certain area of outcomes. One flat line metrics could indicated a domination of another metrics, despite of its importance value.
4. CONCLUSION
From these result, we could evidently showed the relationship of all features to cognitive workload represented by NASA – TLX scores. Cardiac biometrics were found to have moderate relationship with NASA – TLX score, analyzed using the random forest regressor model. Features importance also showed changes of LF/HF ratio from pre to during working state, gave the largest impact to the model, which proven previous studies that found changes of LF/HF during cognition. Despite of that, all cardiac biometrics should be taken into considerations for cognitive workload analysis, as the simultant work of SNS and PNS controlled different part of cardiac functions and biometrics. Taken into consideration of all the result from this study, cardiac biometrics are recommended to be used as cognitive workload indicator. However, larger sample sizes with variations of workload scores, is highly recommended to enhance the performance of the model. Larger datasets could enhanced performance of the random forest regressor and made it possible to be used as workload index prediction tool.
ACKNOWLEDGMENTS
The author would like to acknowledge all parties and participants involved in this research.
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