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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.18 No.4 pp.658-666
DOI : https://doi.org/10.7232/iems.2019.18.4.658

Pulse Rate Measurement Using Tensimeter Tool in Cardiovascular Load Assessment

Nawang Wahyu Widiatmaka*, Muhammad Ragil Suryoputro*, Muhammad Safri Setiawan
Industrial Engineering Department, Universitas Islam Indonesia, Yogyakarta, Indonesia
Corresponding Author, E-mail: nawangwahyuwidiatmaka@gmail.com, ragil.suryoputro@uii.ac.id
May 7, 2019 September 22, 2019 October 18, 2019

ABSTRACT


Physical work load was one consideration of designing work system in order to keep the humanity. Pulse rate measurement was an indirect way to obtain the results of CVL (cardiovascular load) calculation which represented the physiological condition of the employee and it was done manually by looking for radial arteries on the wrist. Measuring the pulse rate that was conducted by finding the pulse manually was a less effective and efficient. It had potentially inhibiting factor that affected the pulse rate during measurement. The purpose of this research was to conduct cardiovascular load assessment by proposing a new concept of pulse measurement that was more efficient and effective using tensimeter tool. Measurement of pulse rate using tensimeter tool were performed to 4 employees for 8 days on the same type of work. A statistical approach with One Sample t-Test technique was performed to examine whether the CVL percentage value differed significantly from the standard secure limits. This study provided scientific contribution in the form of the use of tensimeter tool as a new concept to minimize the intervention of inhibiting factors in indirect cardiac measurement.



초록


    1. INTRODUCTION

    Physical work load was one type of workload that needed to be looked after in assessing the affectivity of work design in fulfilling job tasks (Lean and Shan, 2012). In addition to the fact that this was the only effort to make employees work effectively based on the burden of time, physical workload needed to be considered in order to keep the humanity side. Physical Workload that was too high or too low would cause demotivation (Ivarsson and Eek, 2016; Madeleine, 2010; Horton et al., 2012). Cardiovascular load was an approach that could be used in knowing the level of physical workload (Jakobsen et al., 2014). Cardiovascular load required pulse rate data as the calculation input. Previous research quoted when in getting the pulse rate was able to use indirect measurement (Nurmianto, 1996). Measurement of the pulse or heart became a very important part to obtain the results of cardiovascular load calculations that represented the physiological conditions of employees.

    Fithri and Anisa (2017) explained that measurement of pulse was an indirect way as long as it was done manually by feeling the heartbeat that was in the radial artery on the wrist. Although the method looked convenient, inaccuracy measurement could provide a high error rate. The problem that had occurred so far in doing research was that it was difficult to get the radial artery properly (Mallick and Patro, 2016). It gave influence in the form of anxiety or other intervention to the subject or the researcher himself so that it was vulnerable to cause inaccurate measurement process. Pikaar (2015) proposed that the pulse measurement process was expected to give neither special influences nor interventions to employees so that the resulting pulsation was purely due to work rhythm and used a way which minimizes gender interventions in the measurement process. Measurement of pulse was done by searching for radial artery manually and this was considered as a less effective and potentially inhibiting factor in the measurement process.

    The purpose of this research was to conduct cardiovascular load assessment by proposing a new concept of pulse rate measurement that was more efficient and effective using tensimeter tool so that the measurement of the pulse rate represented the rhythm of work and minimize the gender limitations in the measurement process.

    The purpose of this research is to conduct cardiovascular load assessment by proposing a new concept of pulse rate measurement that is more efficient and effective using tensimeter tool so that the measurement of the pulse rate represents the rhythm of work and minimize the gender limitations in the measurement process.

    2. METHODS

    The research methodology used in this study used case study strategies. The object or subject of the study would be tested more in depth in this case study (Creswell, 2003). Measurement of physical workload based on cardiovascular rhythm was assisted along with the use of OMRON HEM-6121 tensimeter. Data processing involved a one sample t-Test test to determine whether the physical workload of the department was within secure limits or vice versa. There were several stages in the study as illustrated in Figure 1.

    2.1 Participant

    Subject for this study consisted of 4 employees (women) in 1 department and had the same job description and age ranged beetwen 19-21 years, with at least 3 years of working experience in this occupation. The case study was taken in real work from XYZ Company. Cardiovascular measurements were performed twice a day for 1 employee for 8 days. The measurement was performed in each day to get the condition of cardiovascular rhythms before work for resting pulse rate at 6 am – 6.50 am and at peak working conditions for working pulse rate at 2 pm – 3 pm.

    2.2 OMRON HEM-6121 Tensimeter

    A tensimeter was a blood pressure measuring device used on the wrist. However, the OMRON HEM-6121 tensimeter not only displayed blood pressure results, but also detected pulse rate. This was in accordance with previous research that mentions if the pulse rate could be detected at the wrist (Karvonen and Vuorima, 1988). The purpose of using the tensimeter tool was to present another approach in obtaining pulse rate data as the basic input on calculating the percentage of cardiovascular load as an objective measurement of physical workload (Paxion et al., 2014).

    2.2.1 Cardiovascular Load (%CVL)

    Calculation of cardiovascular load percentage (%CVL) was used to determine the classification of physical work-loads based on increased working pulse rate compared with the baseline pulse based on accepted cardiovascular load (Anizar et al., 2018). Related detail of the calculation formula could be studied from Yoopat et al. (2002).

    3. RESULTS

    The results in this study were presented in several sections. The first section described the results of data collection from the literature study in this case generated sets of data retrieval protocols using OMRON HEM-6121 tensimeter proposed by Omronhealthcare (2013). The next section dealt with the explanation of the calculation of physical work load using approach of calculation formula of percentage of cardiovascular load (% CVL). The last section described the test results of one sample t-Test.

    3.1 Tensimeter OMRON HEM-6121 Protocoled

    Prior to the further explanation related to the protocol used in the process of pulse rate data collection with this tensimeter tool, the description related to the introduction of OMRON HEM-6121 tensimeter tool would be explained beforehand. Figure 2 showed the shape and function of the tool display. Figure 3, 4

    The details of the image include the following: (a) wrist cuff, (b) battery bay, (c) monitor, (d) memory button, (e) START / STOP button, (f) systolic blood pressure, (h) the heartbeat symbol, (j) the memory symbol, (k) the heartbeat symbol, (l) the cuff usage guide, (m) the deflation symbol, (n) the symbol of the heartbeat, the battery symbol is weak. The instructions for using the tool in the pulse rate data retrieval protocol are as follows:

    1. Using cuffs on the wrist, with palms facing up and then fastening.

      Cufflinks are not used over clothes with thick sleeves. Ensuring the cuff does not cover the protruding the ulna bulge.

    2. Measurements can be done with the right or left hand. To make a measurement, it should be in a quiet position and sit comfortably. Employees sit with soles on the floor. The employees sit up straight and straightened their backs. The height of the cuff is straight with the height of the chest. Do not bend the wrist back or forward, or clench the hand.

    The results of pulse rate measurements can be seen according to the description (c) in Figure 1. While the related maintenance and other information can be seen in the tool manual. The protocols aimed at the preparation of employees as the measurement respondents are described in Table 1.

    3.2 Cardiovascular Load (%CVL) Calculation(s)

    The percentage of cardiovascular load include work pulse, resting pulse rate, and maximum working pulse rate. The working pulse rate was the average pulse rate at the time a person was working and the resting pulse rate was the average pulse rate before a job begins (Grandjean, 1993). In addition, the maximum pulses were (220-age) for men and (200-age) for women (Tarwaka, 2004). The percentage of cardiovascular load (%CVL) could be calculated based on the following formula (Manuaba, 2000):

    % C V L = w o r k i n g   p u l s e r e s t i n g   p u l s e m a x i m u m   p u l s e r e s t i o n g   p u l s e × 100 %
    (1)

    Working and resting pulse rates were obtained through observation. The age information data of each employee to determine the maximum pulse rate was collected by conducting interviews. The percentage of cardiovascular load (% CVL) falling within the safe working load category was 30%. Table 2 described the classification of physical workloads by % CVL (Ismaila et al., 2013).

    Based on data observation conducted for 8 days to 4 employees (women) in the department at case study, the calculation of percentage of cardiovascular load (% CVL) was presented in Table 3.

    The following Figure 5 describes the physical workload condition of the result of a %CVL calculation adjusted to the %CVL secure limit.

    3.3 One Sample t-Test Examination

    One sample t-test was an analytical technique to compare a sample. This technique was used to test whether a particular value differed significantly by the average rate of a sample (Arifin, 2017). In this hypothesis test, a sample was taken which was then analyzed whether there was a difference in the average of the sample as the standard value specified. The one sample t-Test test was performed using SPSS 22 software. The default value or test value specified was 30% (0.3) which considered as the safe boundary of a physical workload. Ho was accepted when the value of Sig. (2-tailed)> 0.05, while Ha was accepted then if the value of Sig. (2-tailed) <0.05. The hypothesis of one sample t-Test was as follows.

    • Ho: μ = 0, 3 = There was no difference in the mean score of %CVL with the %CVL secure limit

    • Ha: μ = 0,3 = There was a difference in the mean score of %CVL with the %CVL secure limit

    Based on the result of one sample t-test on %CVL that had been done, the overall employee has sig. (2-tailed) = 0.00. Sig value. (2-tailed) <0.05 so that Ha is received so that there is a significant difference between the average value of employee %CVL with the %CVL secure.

    4. DISCUSSION

    The discussion raised in this section deals with how the tensimeter tool can answer the issues raised in the background of the problem. In addition, this discussion raised the analysis of physical workload level condition experienced by related departments based on the percentage approach of cardiovascular load (%CVL) combined with one sample t-test.

    4.1 Effectivity of Tensimeter Usage

    The use of tensimeter was an indirect measurement method to measure the working pulse rate. Pulse rate was a good estimator of metabolic rate, except in an emotional state. The light weight category of a physical workload was based on the metabolism of respiration, body temperature, and pulse rate (Jakobsen et al., 2014). The used of the work pulse to assess the weight of the workload had several advantages, beside the simplicity, fast, and cheap, it did not require expensive equipment and the result was quite accurate and did not disturb or hurt the person who was being examined (Fithri and Anisa, 2017). Pulse rate measurements could be done in various ways by feeling the heartbeat that was in the radial artery on the wrist, listening to pulse rate with stethoscope, and the last was using ECG (Electrocardiograph), which measured the measured electrical signal of the heart muscle on the surface of chest skin (Fithri and Anisa, 2017).

    Prior studies, that examine the physical workload, was in form of measurement by feeling the heartbeat on the wrist with a 10-beat approach. In this way, the researcher should be alerted to find the radial artery to be able to do 10 pulses. Research that had been done previously mentioned if this way had not been done successfully and considering that every human being had a different sense of sensitivity. The thing that often happens was that the researcher did a conclusion that was not in accordance with the condition because he was unable to calculate 10 pulses well. In addition to this, a way that required researchers to hold the hands of respondents in finding radial arteries caused discomfort among researchers and respondents by gender differences (Webster, 1987). In some cases, such conditions were unacceptable to the respondent. Of course it was a factor to be considered when researchers used this approach. This was because ethics in research required researchers to pay attention to aspects of comfort and privacy of respondents.

    The second way used stethoscopes which rarely done in research given by the special skill demanded in using the device in hearing the sound of a heartbeat (Webster, 1987). Basically, these special skills were intended to be sensitive and meticulous in detecting pulse rate sounds. The biggest obstacle was the limitation of gender differences since the use of this tool was on the sensitive part and was the privacy of women (Evans, 2002). It was certainly violating the ethics of research if in this case researchers were men. The inhibiting factor caused the non-fulfillment of the flexibility aspect for the use of stethoscopes in pulse rate measurements. Thus, researchers considered these aspects in the use of this method.

    While ECG (Electrocardiograph) in previous studies was often used, however, in this case, there were limitations or inhibiting factors in the use of this method. The ECG (Electrocardiograph) measured the electrical signals of the heart muscle on the surface of the breast skin with telemetry stimulation. The use of this method used in previous studies was aimed at activities that did not require much coarse body movement (Hatfield et al., 1984; Crews and Landers, 1993). This was because this tool can run with telemetry. If the case study encountered was activities that require the respondent to move dynamically from one place to another, then the tool was not relevant to use. It was precisely when this tool was used would cause the employee’s limitations in work and caused the measurement process to be unnatural (Apparies et al., 1998). In addition, the used of this tool required more funding than the previous two ways (Myrnerts Höök et al., 2018).

    This research was a follow-up of previous studies that combined processes from sensors to health monitoring systems by Nakamura et al. (2011). Sensors used include heart rate sensors and blood pressure. The data received by the sensor was processed together with attention from the timestamps for the synchronization process. This tool was a type of digital measurement that works based on the method of oscillometry. In determining the heart rate, this tool used a pressure sensor as a transducer that would detect blood pressure and changes in oscillation signals due to heart rate. When the measurement time was taken, this tool in its measurement could directly capture the heart rate. This condition would minimize the change in fluctuating heart rate due to external factors other than the work activities carried out. Tools such as an oxymeter in its use would display the results of heart rate measurements that continuously changed throughout the measurement process. The tool could not capture a summary of the measurement results directly. The condition would be an obstacle in making decisions from the measurement of heart rate when working.

    Based on the explanation of various inhibiting factors in pulse rate measurement, the researcher had initiated to accommodate the method of measuring the pulse rate present in the radial artery at the wrist by using a tensimeter tool. In this way, researchers did not necessarily have to hold the hands of respondents directly in search of radial arteries because the process had been replaced by a tensimeter tool. Researchers only needed to apply the tool to the wrist and implemented the protocols described in the previous section. The results of the heartbeat would appear by itself after the tool started to detect the employee’s heartbeat for 1 minute. The used of tensimeter tool had succeeded in minimizing the inhibiting factor of measuring the sensitivity level of the researcher. The process of collecting pulse rate data in this study was given feedback by all employees if the used of tensimeter tool provided more comfort. Comfort meant by the employee was the absence of hand-touching process between male researchers and women employees. Even after the employee understood the protocol and the used of this tensimeter tool, the measurement process was done by the employee itself. In such cases, the employee remained in the process of control in ensuring the protocol of the use of the appliance remained in place properly. Thus, the tensimter tool also successfully minimized other inhibiting factors of measurement, namely the limitation of gender differences.

    4.2 Physical Workload

    Based on data collection of pulse rate during 8 to 4 days among woman employees, the result of calculation of %CVL in Table 2 showed when overall employees were in condition of secure physical workload. The condition of the physical workload indicated if the entire employees in performing the task demands did not experience any defeat. The average value of the entire CVL% Employee (department) was at 9%, while the value of the secure limit% CVL was at a value of 30%.

    The first data obtained under FIG. 5 was the existence of a %CVL value of one employee exceeding the average CVL value of the entire employees (in one department) as the lower limit, i.e. Employee 4. It was indicated when Employee 4 had the lowest physical workload among 4 other employees, although the actual difference between the %CVL value in each Employee was slightly narrow. The second information related to the difference between the %CVL values of each employee to the %CVL secure limit. Both values had a rate differences, but whether the value of the %CVL Employee had a significant average difference with the test value (30% = 0.3) or no one sample t-test was required to process the data more deeply. In the one sample t-Test, the value of the %CVL secure limit was used as a measurement.

    Based on the test results presented in the previous section, Ha was accepted so that there was a significant difference between the average values of employee %CVL with the %CVL secure limit. In this case, the overall value of the employee %CVL was smaller than the %CVL secure limit so that the one sample t-Test results showed the information when the overall physical workload of the employee in the department was very secure. The results of the one sample t-test that showed a significant difference indicated that if the physical workload conditions received by all employees were far to change beyond the secure limit value of% CVL. But, this was interesting to be reviewed based on the significant difference between the values of employee %CVL with a secure limit. Physical workload would be at the ideal value when the value of %CVL employees was below the secure limit value of % CVL, but close to or not had a significant difference with the secure limit value of% CVL. These conditions could be an important consideration for companies to optimize physical workloads that were at low %CVL value. If this was not done, then at a certain time in the future could make a decrease in motivation for employees (Hollnagel and Bye, 2000).

    However, this consideration needed to be balanced with other variables that may had an effect on the assessment of the physical workload. In this case, the relationship between physical workload conditions with work productivity could be a potential for further research. If the productivity of work was in a productive condition, then the low %CVL value might not be of particular concern. It maenad that in this case, no indirect employment factors such as idle activity, chatting, etc. affect the low %CVL value. These results indicated that the operator had been able and successfully adjusted the demands and work characteristics undertaken in physiological aspects (Yoopat et al., 2002).

    This should become one of the particular concerns since the assessment of the physical workload carried out in this study focused only on the physiological rhythm as a result of the demands assigned to the employee. This study did not consider other aspects related to other physical workloads such as pain and sore complaints or could be called Cumulative Trauma Disorders (CTD’s). Based on the calculation of %CVL, employee was considered not to feel fatigue physiologically, but it might happen actually when employee was experiencing complaints daily due to the demanded of the task given. The discussion would be a limitation in this research and therefore could be a potential for further research related to the discussion. On the other hand, there had been previous studies that had described conditions of cardiovascular loads and complaints of discomfort in body parts in the same study subjects (Sari et al., 2016). The study provided suggestions for actions taken against steps to reduce the parts used for productivity, although in the study did not explain the relationship between conditions of cardiovascular burden, complaints of body parts, and productivity. The things that were important for further research were carried out successfully with ergonomics with decreased cardiovascular load and complaints of body pain, could provide the potential in the form of the amount of demotivation that occured at certain points productivity decreases (Ivarsson and Eek, 2016; Madeleine, 2010; Horton et al., 2012). This exposure could be an additional potential for conducting research related to the decision.

    However, regardless of the framework of this study, what should be discussed again from the results of this study was related to the factor that caused the overall employees were not feeling fatigue based on the calculation of %CVL. the absence of significant fatigue was influenced by the absence of exposure to high temperatures in the workplace environment. This research site had a standard room temperature that must been met, namely in the temperature range 20 - 23°C for relation to the quality of production. Environmental temperature conditions had an influence on aspects of human physiological conditions (Yoopat et al., 2002). Previous research had explained that the level of human immunity in the face of fatigue was influenced by feelings that had been accustomed to the length of time someone did the job (Kusgiyanto et al., 2017). The resulting form of workload might be due to work experience (Munandar and Ashar, 2001). In addition to physical workload factors, the number of subjects factors could also be a problem that could be an opportunity to conduct further research. As a rule, in practice, a larger sample size had greater statistical power (Prajapati et al., 2010). However, the number of subjects involved was small because of the location of the study only had three subjects. Therefore, in the next study needed to be done with a larger number of subjects to get the better confirmation from the study results. Also, in further research could be made a comparison of heart rate measurements with tools that were more sophisticated, like smartwatch that was reasonably accurate with a high degree of correlation to measure heart rate (Phan et al., 2015) to understand the effectiveness of tensimeter tool. Another factor that had the potential to influence was the absence of relationships that affected the type of task demand to the physical workload of the operator, meaning that the type of task demand was more dominant in the influence mental workload or time. This condition could be a potential to be discussed as further research to assess the condition of the workload of all variables, namely the physical, mental, and time workload. Also, the number of research samples and comparisons with other tools had the potential to complement future research.

    5. CONCLUSION

    Based on the data processing that had been done, the results indicated when the overall physical workload of the employee was in a safe condition for non-occurrence and the value had significant differences with the standard limits of safe physical work load. These conditions could not be regarded as ideal conditions considering that it needed to be balanced with other research that supported the results of the data, in this case served as the potential of further research. However, based on the ease and high level of effectiveness of the use of tensimeter tool, this research contributed to the scientific form of proposing the use of tensimeter tool as a new concept to minimize the inhibition factor intervention in performing indirect pulse measurements. Conceptually, this measurement method could answer the problem issues that had been presented in the research background. Even this method provided a side that was more beneficial for researchers in the measurement process, in terms of time and easy to use. However, this research was still in the literature review stage and there had been no testing of pulse measurement data with comparison methods or other tools. Thus, it becames a potential for further research.

    Figure

    IEMS-18-4-658_F1.gif

    Research methodology

    IEMS-18-4-658_F2.gif

    Display tool of omron hem-6121 tensimeter.

    IEMS-18-4-658_F3.gif

    Manual usage of the tool (1).

    IEMS-18-4-658_F4.gif

    Manual usage of the tool (2).

    IEMS-18-4-658_F5.gif

    Condition of physical workload.

    Table

    Classification of preparation procedures for measurement of physical workloads

    Workload classification based on %CVL

    Calculation of cardiovascular load percentage (%CVL)

    REFERENCES

    1. Anizar, Matondang, A. R. , and Sibarani, J. A. (2018), Application work risk of manual material handling operators using different lifting methods, IOP Conf. Series: Materials Science and Engineering, 309, 17.
    2. Apparies, R. J. , Riniolo, T. C. , and Porges, S. W. (1998), A psychophysiological investigation of the effects of driving longer-combination vehicles, Journal Ergonomics, 41(5), 581-592.
    3. Arifin, J. (2017), SPSS 24 Untuk Penelitian dan Skripsi, PT. Elex Media Komputindo, Jakarta.
    4. Chirakanphaisarn, N. , Thongkanluang, T. , and Chiwpreechar, Y. (2016), Heart rate measurement and electrical pulse signal analysis for subjects span of 20-80 years, Journal of Electrical Systems and Information Technology, 8, 1-9.
    5. Creswell, J. W. (2003), Research Design: Qualitative, Quantitative, and Mixed Methods Approach (2nd Ed.), Saga Publication, California.
    6. Crews, D. and Landers, W. (1993), Electroencephalographic measures of attentional patterns prior to the golf putt, Medicine and Science in Sport and Exercise, 25(1), 116-126.
    7. Evans, J. A. (2002), Cautious caregivers: Gender stereotypes and the sexualization of men nurses’ touch, Journal of Advance Nursing, 40(4), 441-448.
    8. Fithri, P. and Anisa, W. F. (2017), Pengukuran beban kerja psikologis dan fisiologis pekerja di industri tekstil, Jurnal Optimasi Sistem Industri, 16(2), 120-130.
    9. Grandjean, E. (1993), Fitting the Task to the Man (4th Ed.), Taylor and Francis Inc, London.
    10. Hatfield, B. D. , Landers, D. M. , and Ray, W. (1984), Cognitive processes during self paced motor performance: An electroencephalographic profile of skilled marksmen, Journal of Sport Psychology, 6, 42-59.
    11. Hollnagel, E. and Bye, A. (2000), Principles for modelling function allocation, International Journal of Human- Computer Studies, 52(2), 253-265.
    12. Horton, L. M. , Nussbaum, M. A. , and Agnew, M. J. (2012), Effects of rotation frequency and task order on localised muscle fatigue and performance during repetitive static shoulder exertions, Ergonomics, 55(10), 1205 -1217.
    13. Ismaila, S. O. , Oriolowo, K. T. , and Akanbi, O. G. (2013), Cardiovascular strain of sawmill workers in South- Western Nigeria, International Journal of Occupational Safety and Ergonomics (JOSE), 19(4), 607-611.
    14. Ivarsson, A. and Eek, F. (2016), The relationship between physical workload and quality within line-based assembly, Journal Ergonomics, 59(7), 913-923.
    15. Jakobsen, M. D. , Sundstrup, E. , Persson, R. , Andersen, C. H. , and Andersen, L. L. (2014), Is Borg’s perceived exertion scale a useful indicator of muscular and cardiovascular load in blue collar workers with lifting tasks? A cross sectional workplace study, Eur J Appl Physiol, 114(2), 425-434.
    16. Karvonen, J. and Vuorima, T. (1988), Heart rate and exercise intensity during sports activities, Sport Medicine Journal, 5(5), 303-311.
    17. Kusgiyanto, W. , Suroto, and Ekawati (2017), Analisis hubungan beban kerja fisik, masa kerja, usia, dan jenis kelamin terhadap tingkat kelelahan kerja pada pekerja bagian pembuatan kulit lumpia di kelurahan kranggan kecamatan semarang tengah, Jurnal Kesehatan Masyarakat, 5(5), 413-423.
    18. Lauer, M. , Froelicher, E. S. , Williams, M. , and Kligfield, (2005), Exercise testing in asymptomatic adults: A statement for professionals from the American heart association council on clinical cardiology, Subcommittee on Exercise, Cardiac Rehabilitation, and Prevention, Circulation, 112(5), 771-776.
    19. Lean, Y. and Shan, F. (2012), Brief review on physiological and biochemical evaluations of human mental workload, Human Factors and Ergonomics in Manufacturing & Service Industries, 22(3), 177-187.
    20. Madeleine, P. (2010), On functional motor adaptations: from the quantification of motor strategies to the prevention of musculoskeletal disorders in the neckshoulder region, Acta Physiologica, 199(s679), 1-46.
    21. Mallick, B. and Patro, A. K. (2016), Pulse rate monitoring system using finger tip through arduino and processing software, International Journal of Science, Engineering and Technology Research (IJSETR), 5(1), 84-90.
    22. Manuaba (2000), Ergonomi, Kesehatan dan Keselamatan Kerja, Guna Widya, Surabaya.
    23. Munandar and Ashar, S. (2001), Psikologi Industry dan Organisasi, UI Press, Jakarta.
    24. Myrnerts Höök, S. , Pejovic, N. J. , Marrone, G. , Tylleskär, T. , and Alfvén, T. (2018), Accurate and fast neonatal heart rate assessment with a smartphone‐based application: A manikin study, Acta Paediatr, 107(9), 1548-1554.
    25. Nakamura, M. , Nakamura, J. , Lopez, G. , Shuzo, M. , and Yamada, I. (2011), Collaborative processing wearable and ambient sensor system for blood pressure monitoring, Sensors, 11(7), 6760-70.
    26. Nurmianto, E. (1996), Ergonomi, Konsep Dasar dan Aplikasinya (1st Ed.), Guna Widya, Surabaya.
    27. Omronhealthcare (2013), HEM-6121 Instruction Manual, Available from: https://omronhealthcare.com.bd/resources/HEM-6121-AP.pdf.
    28. Palatini, P. and Julius, S. (1997), Heart rate and the cardiovascular risk, J Hypertens, 15(1), 3-17.
    29. Paxion, J. Galy, E. , and Berthelon, E. (2014), Mental workload and driving, Frontiers in Psychology, 5, 1-11.
    30. Phan, D., Siong, L. Y., Pathirana, P. N., and Seneviratne, A.2015), Smartwatch: Performance evaluation for long-term heart rate monitoring, Proceedings of the 2015 International Symposium on Bioelectronics and Bioinformatics (ISBB), Beijing, China, 1-9.
    31. Pikaar, E. (2015), Finding alternative ways to measure mental workload while driving, Unpublished Master Thesis, Universiteit Leiden, Faculty of Social & Behavioral Sciences. Leiden, The Netherlands.
    32. Prajapati, B. , Dunne, M. , and Armstrong, R. (2010), Sample size estimation and statistical power analyses, Optometry today, 16(07), 10-18.
    33. Sari, A. D. , Suryoputro, M. R. , Pramaningtyas, M. D. , Putra, P. S. , and Maulidyawati, S. B. (2016), Work physiology evaluation of laundry workers, IOP Conference Series: Materials Science and Engineering, 105, 012034.
    34. Savonen, K. P. , Lakka, T. A. , and Laukkanen, J. A. (2006), Pulse rate response during exercise test and cardiovascular mortality in middle-aged men, Eur Heart J., 27(5), 582-588.
    35. Tarwaka (2004), Ergonomi untuk Keselamatan Kesehatan Kerja dan Produktivitas, Penerbit UNIBA Press, Surakarta.
    36. Webster, A. T. (1987), Now that we have pulse oximeters and capnographs, we don’t need precordial and esophageal stethoscopes, Journal of Clinical Monitoring, 3(3), 191-192.
    37. Yoopat, P. , Toicharoen, P. , Glinsukon, T. , Vanwonterghem, K. , and Louhevaara, V. (2002), Ergonomics in practice: Physical workload and heat stress in Thailand, International Journal of Occupational Safety and Ergonomics (JOSE), 8(1), 83-89.