993Sensors and Materials, Vol. 31, No. 3 (2019) 993–1006
MYU Tokyo
S & M 1830

Study on Environmental Parameters
and Wearing Electrocardiogram
Yang-Han Lee,1 Hsien-Wei Tseng,2* Yu-De Liao,1Ting-Wei Lin,1 Yi-Lun Chen,1 and Ying-Sen Ho112

Department of Electrical and Computer Engineering, Tamkang University, Taiwan, R.O.C.
School of Mathematics and Information Engineering, Longyan University, Longyan, Fujian, China(Received November 23, 2018; accepted February 15, 2019)

Keywords: IoT, cloud database, ECG, environmental sensing

Over the years, environmental changes have made people focus their attention on thequality of their surrounding areas and work environment. However, there are some dangerous
substances in areas suitable for human survival and work. People often ignore harmful
substances in the air (e.g., substances determined by temperature and humidity, carbon dioxide,and other harmful gases), resulting in physical problems. These substances are often too small
to be observed and will severely jeopardize a human body that has been long fixed in the sameplace. Therefore, the purpose of this study is to assist humans in detecting the presence of
hazardous substances in their living and work environments, and to improve such environments
through sampling and inspection. Through this effort, humans can survive in a living andworking space, and live and work in a comfortable environment. On the basis of the Arduino
development edition, in this study, we transfer data to a cloud database through WiFi andcombine the cloud database with the heart rate from an electrocardiogram to detect the humanheart rate in the work environment to achieve 24 h real-time monitoring at home or in theworkplace. By improving the flow field of the interior space, we can further reduce the indoor
harmful substance level and optimize human life and work quality. For life and industrial
hygiene safety consciousness awakening, in this study, we can promote human safety in theliving and work environments by using a sensor to prevent potentially dangerous genetic lesions
or derivatives of a disease. In addition, the scope of this study is not limited to the living spaceof people but extends to factories or other places with harmful substances. In a high-risk work
environment, the equipment can be used to conduct risk preanalysis and detection in place of ahuman to achieve full assessment or environmental improvement, which eliminates the safetyconcerns of people in the environment.

1.

Introduction

Over the years, environmental changes have made people focus their attention on thequality of their surrounding areas and work environment [Development of the core technology*

Corresponding author: e-mail: hsienwei.tseng@gmail.com
https://doi.org/10.18494/SAM.2019.2223
ISSN 0914-4935 © MYU K.K.
https://myukk.org/

994Sensors and Materials, Vol. 31, No. 3 (2019)of mobile-assist robots for the elderly health via Artificial Intelligence and Internet of Things(project number: MOST 107-2221-E-032-047-)]. However, there are some dangerous substancesin areas suitable for human survival and work. People often ignore harmful substances in theair, resulting in physical problems. Therefore, we hope to achieve real-time monitoring ofenvironmental parameters and study the correlation between the heart rhythm of subjects andenvironmental parameters by using a wearable electrocardiogram.(1–5) The electrocardiogramis one of the most important indicators of heart health. It can achieve a certain diagnostic effectthrough a noninvasive method.
In a previous study,(6) monitoring the use of wireless sensor network (WSN) technology tocollect air quality data was proposed and the high potential of such technology was discussed.
In the currernt study, we demostrated a monitoring and visualization approach to help big
data play a role in the health and safety decisions of the shipping industry. We also identified
other potential applications of WSN technology and the visualization of big data in the work
environment, e.g., to monitor worker safety in high-risk industries and commodity quality insupply chain management.
In another previous study,(7) the variation of electrocardiography (ECG)’s low frequency/
high frequency (LF/HF) ratio was proposed. As shown in Fig. 1, the sympathetic nervous
system plays an important role in thermal discomfort. Once sympathetic excitation is achieved,
temperature regulation disturbs the heat of the human body. Given the difference in LF/
HF ratio between the comfort and discomfort levels shown in Fig. 2, the LF/HF ratio can beconsidered as a physiological indicator of the thermal comfort level of the human body, as
shown in Fig. 3.
Heart rate variability (HRV) is proposed in Ref. 8 as the index of thermal comfort in an
office environment. Unlike existing mechanisms, this approach will allow people to predict theFig. 1.

Change in LF/HF ratio.

Fig. 2.
levels.

LF/HF ratio at comfort and discomfort

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Fig. 3.

EDR’s LF/HF ratio at comfort level.

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Fig. 4. Mean very low frequency power (VLF),
mean R–R interval (RR), and HRV for all subjects.

physical response of thermal comfort to the surrounding heat, as shown in Fig. 4.
Most of the existing methods of environmental parameter detection are fixed-point
measurement, and real-time data uploading cannot be achieved. Therefore, we hope tocombine the cloud database with a web platform, so that viewers can view real-time data ofenvironmental parameters anywhere and anytime. In this paper, real-time sensing of theparameters in the environment is proposed, and the arrhythmia analysis of wearing ECG isconducted for the personnel in the environment to observe the effect of the parameters in theenvironment on the heart rhythm of the subject. It will be confirmed by experiments that theheart rhythm of people changes with temperature, humidity, and carbon dioxide (CO2) level,from which physiological information and physiological conditions of people in the environment
can be observed. Thus, the method proposed here can be applied to various environments athome or in the workplace in the future for research and discussion.(9–14)

2.

Research Methods

2.1Structure

The structure used in this study is illustrated in Fig. 5. Arduino real-time measured data
will be uploaded to the cloud, the WebCam camera will be employed for 24 h of real-timemonitoring, the HRV of the subjects will be measured with a heartbeat band, and the above
data will be displayed on a phone via Bluetooth to verify whether people in the environment
feel physically ill from changes in the environment. On that basis, we can improve and achieve
healthy environmental quality and maintain healthy living and work environments.(15–20)
2.2

Introduction of hardware

For the hard equipment used in this study, we employ an Arduino D1 development
board combined with a DHT22 temperature and humidity module, an MG811 CO2 sensor

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Fig. 5.

Fig. 6.

(Color online) Structure.

(Color online) BeneGear Inc. ECG125.

Fig. 8.

Fig. 7.

(Color online) Heartbeat band.

(Color online) Observation of ECG signal mode based on Benegear Inc. HRM.

module, a PM2.5 dust sensing module, and an ESP8266 WiFi module to detect the uploaded
environmental data. Subjects are required to wear the heartbeat equipment ECG125 (BeneGearInc.) to obtain the ECG, as shown in Figs. 6–8. Logitech C107 WebCam is employed to present
a webpage-based video.

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2.3

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Server architecture and web page presentation

WampServer is a development environment for Windows Web consisting of Apache2, PHP,and MySQL database. PHP MyAdmin can be employed to manage the database, as shown in Fig. 9.
PHP page syntax can be used to capture database data and display them on the web page, asshown in Fig. 10.

Fig. 9.

(Color online) Cloud database.

Fig. 10. (Color online) Web interface.

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2.4

Sensors and Materials, Vol. 31, No. 3 (2019)

System flowchart

The measurement is started. The camera, Arduino development platform, and heartbeat
band are simultaneously turned on and the camera records the status of the experiment. The
Arduino development platfrom measures the environmental data and uploads it to PHP. Inaddition, the ECG is measured through the heart band, and the ECG data is collected by amobile phone. After collecting environmental and physiological data, the correlation between
environmental quality and physical health is analyzed, as shown in Fig. 11.

3.

Results and Comparison

3.1

Correlation between PM2.5 and ECG

As shown in Fig. 12, the subject was cooking in an airtight kitchen. The PM2.5 and ECG
were measured, and then their correlation was analyzed. In the morning experiment that started
at 00:05, the cooking time was 00:10, cooking was done at 00:30, and the measurement ended
at 00:33. Figure 13 shows the relationship of HRV with temperature and humidity. Figure 14
shows the correlation between HRV and PM2.5 of the subject. Figure 15 shows subjects’ ECG.

Fig. 11. (Color online) System flowchart.

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Fig. 12. (Color online) Test environment (kitchen).

Fig. 13. (Color online) Relationship between HRV and temperature and humidity.

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Fig. 14. (Color online) Correlation between HRV and PM2.5 of the subject.

Fig. 15. (Color online) Subjects’ ECG.

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3.21001

Correlation between CO2 and ECG

As shown in Fig. 16, the subjects first measured CO2 and ECG in an airtight classroom.
During the measurement, fans were added to improve air convection, as shown in Fig. 17.
Finally, the correlation between CO2 and ECG was analyzed. The experimental test lasted from
15:42 to 17:50, the measurement was started at 15:42, and the half-time was 16:50. Fans wereadded to improve the indoor flow field. Then, the measurement was started at 17:10 and thenended at 17:50. A total of six subjects were included.
Results of environmental classroom under test
15:42 Measurement started
16:50 Half-time
17:10 Measurement restarted(Improved indoor flow field)
17:50 Measurement ended
Figure 18 shows CO2 changes in classroom. Figure 19 shows the relationship of HRV withtemperature and humidity of subject 1. Figure 20 shows the relationship between HRV and CO2of subjects 1. Figure 21 shows the heart rate of subject 1.

Fig. 16.

(Color online) Measurement of classroom environment.

Fig. 17.

(Color online) Locations of convection fans.

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Fig. 18. (Color online) CO2 changes in classroom.

Fig. 19. (Color online) Relationship between HRV and temperature and humidity of subject 1.

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Fig. 20. (Color online) Relationship between HRV and CO2 of subject 1.

Fig. 21.

(Color online) Heart rate of subject 1.

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4.

Sensors and Materials, Vol. 31, No. 3 (2019)Conclusion

In this study, we primarily developed a web platform that is capable of detecting substancesin the environment in real time and uploading them to a cloud database, allowing people towear a load ECG device to compare the changes and determine the correlation between thesubstances in the environment and ECG. Many different situations were studied to compare theHRV analysis charts of the substances in air, and it was found that many changes indeed affectthe heart rhythm of people.
The effects of CO2 and PM2.5 were found to be most obvious in many experiments. Somestudies have also attempted to increase convection in air to reduce parameters that can affect theheart rhythm of the human body, and the electrocardiogram can sometimes slightly differ sincepeople’s behaviors might affect the changes in the electrocardiogram. Thus, these situations arealso aspects to be further analyzed. The major contribution of this study is the introduction ofan environmental sensor into a platform, introducing the real-time sensing of the human bodyto analyze the effects of environmental parameters on the human heart rhythm and achieving
the convenience of real-time sensing using a wearable device.

Acknowledgments
This work was supported by a research project of the Ministry of Science and Technology,
Republic of China, under grant MOST 107-2221-E-032-047.

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About the Authors
Yang-Han Lee received his B.S., M.S., and Ph.D. degrees in electricalengineering from National Taiwan University, Taipei, in 1987, 1989, and 1991,
respectively. He joined the Department of Electrical Engineering, Tamkang
University, Taipei, in 1994 and now serves as a professor. His main researchinterests include artificial intelligence, internet of things, and wirelesscommunication systems.
Hsien-Wei Tseng received his B.S. degree in electrical engineering fromNational Kaohsiung University of Applied Sciences in 2002 and his Ph.D.
degree in electrical engineering from Tamkang University in 2010. He joined
the School of Mathematics and Information Engineering, Longyan University,Longyan, Fujian, China, in Aug. 2017 and now serves as a professor. His
main research interests include system design and performance evaluation in
IoT and wireless communication systems.
Yu-De Liao received his B.S. degree in computer and communicationengineering in 2011 from DE LIN Institute of Technology and his M.S. degreein electronic engineering in 2013 from Tamkang University. He is currently aPh.D. student at the Department of Electrical Engineering, Tamkang
University. His main research interests include internet of things and wirelesscommunication systems.
Ting-Wei Lin received his B.S. and M.S. degrees in Electrical Engineeringfrom Tamkang University, Taipei, in 2013, and 2015, respectively. He iscurrently a Ph.D. student at the Department of Electrical and Computer
Engineering, Tamkang University. His main research interests include
artificial intelligence, internet of things, and embedded systems.

Yi-Lun Chen received his B.S. and M.S. degrees in electrical engineeringfrom Tamkang University, Taipei, in 2015 and 2017, respectively. He iscurrently a Ph.D student at the Department of Electrical and Computer
Engineering, Tamkang University. His main research interests include
artificial intelligence, internet of things, and embedded systems.

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Ying-Sen Ho received his B.S. and M.S. degrees in electrical engineeringfrom Tamkang University, Taipei, in 2009 and 2011, respectively. He iscurrently a Ph.D. student at the Department of Electrical and Computer
Engineering, Tamkang University. His main research interests include
internet of things and wireless communication systems.