Smart Patient and Environment Monitoring System with Wireless Notification Network Utilized by ESP8266


AuthorsDonne Calixto Mabugat, Romo Pasaporte Jr., Ian Kristoffer Garrucho, Phillip Raymund R. De Oca, Mark Angelo Tarvina
Volume1
Date PublishedSeptember 27, 2024
Date UpdatedSeptember 21, 2024


Abstract

Healthcare monitoring is now vital to help prevent Cardiovascular disease (CVD), a leading cause of death worldwide. The researchers' objective is to develop a patient and environment monitoring system which measures the patient's heart rate, room temperature, ambient humidity, and air quality content. To assess the accuracy of the device sensors when compared with commercially available devices, 100 data points, each from 5 different subjects for the pulse oximeter, and 33 data points from 8 different laboratory rooms for temperature and humidity measurements were gathered with the International Organization of Standards for accuracy of pulse oximeters, thermo-hygrometers and digital thermometers used as reference. The patient and environment monitoring system was accurate and industry qualified, with the pulse oximeter's accuracy transmitting beats per minute data, ranging from 0.24% to 3.36%, temperature sensors at 0.35% to 4.72%, and onboard humidity sensors accurate as much as 1.07% and the minimum accuracy standing at 4.79%.



Introduction

Monitoring in the medical field is the continuous observation and information management of a patient’s clinical condition parameters through several medical parameters over time to maintain safety [1][2][3] .

Emerging technologies, smartphones, and Wearable Health Devices (WHDs) made real-time monitoring of vital metrics at home, work, and in any clinical environment with minimized patient contact possible [4][5][6] .

Although monitoring technologies have contributed heavily to the advancement of healthcare monitoring systems, there are issues and technological barriers to the efficiency of devices and systems to process information producing accurate data [7][8][9][10] . Also, there is still no specific library for the oxygen saturation measurement in technologies for tracking blood oxygen levels.

It is estimated that cardiovascular disease (CVD) is the leading cause of death, roughly about 30% globally, primarily affecting low- and middle-income economies than economically advanced countries combined [11][12]. The worldwide increase in CVD follows a parallel trend in the elderly population. The percentage of people over the age of 60 is fast-growing and is faster than any other age group [12] .

Globally, there is health and well-being promotion to reduce premature mortality by one-third [13] . However, the Agency for Healthcare Research and Quality (AHRQ) has documented inequalities in treatment that rural communities face when compared to urban regions. Hospitals in rural areas are remote, resulting in a shortage of experts and worse outcomes in Heart Attacks, as people need to travel for extended periods [14][15] .

Heart rate monitoring is vital to track the body’s physiological adaptation in response to physical activities and the environment [16] . The temperature has a high impact on CVD when combined with high humidity. There is also a positive correlation between CVD mortality and relative humidity. Also, both have common effects and play a role in the way bodies react to weather conditions. Pressed with this issue, the researchers innovate a device integrating multiple sensors to mitigate the risks associated with heart diseases.



Methodology



System Design and System’s Schematics

The purpose of the device was to measure the heartbeat of a patient. The device was designed and developed in steps, with the functionality shown in Figure 1 guiding the process.

Figure 1. Block Diagram for Device Functionality



Motherboard Assembly

Passive and main components were audited and installed using the schematic diagram, capacitors were soldered starting from terminal C1 to terminal C18. Resistors from terminals R1 to R21 were also soldered. 1N414BW was soldered on D1 and D2 terminals located beside the buzzer slot and 2N7002 N-Channel Enhancement Mode Mosfet diode on the Q1 terminal. The number of female pin headers needed was determined, cut, and aligned in assigned slots including the SD Card female pin header slot, Featherboard port, HC05 Bluetooth module port, Max 30102 port, and other slots for main components. The main components, the HDC1080 temperature, and humidity sensor were also installed. It was placed above the C17 terminal, and a continuity test was performed immediately. MQ-2 Gas sensor was also installed. Pins of the RTC module were bent downward where the DS3231 module Real-Time-Clock was inserted through the pinholes. PCF8574T I2C LCD Interface was also installed in the 1 st until the 16th pin with the pins on the side also soldered thereafter. The LCD 16x02 module and the 3-12V buzzer were also placed on the pin slots. All passive and main components were reinforced with flux on the power plain and checked if all were correctly soldered.



Starting up of a Device

The device was set following the program flow in Figure 4. The LCD was first set to display a “Welcome” message prompting the user for the Wifi SSID/ Password. In Setting up Esp8266 in the Arduino ide, Adafruit Feather HUZZAH ESP8266 must be selected from the tools in the dropdown board. CPU frequency will then be set to 80 MHz and for upload speed select 115200 baud. A database via Google Firebase was constructed to establish a connection to the firebase. To connect the microcontroller to the database, copy its hostname and paste it into the Arduino code. To link HDC1080 Temperature and Humidity sensor encodes its manufacturer ID, device ID, device serial number and initializes it during the debug mode. By default, the MQ2 Flying fish gas detector sensor was looped to the rest of the peripherals. The MAX30102 parameters were then calibrated into default settings. Lastly, buttons are linked to the rest of the peripherals when prompted by the LCD interface or serial monitor.



Website Development

For the concept of device functionality, a user-friendly web application was developed using Javascript as a programming language. Necessary codes were typed to link the website's data to Google Firebase so results in the graph will be presented in a meaningful and informative manner.

Figure 5. Website Application with Warning System



Experimentation

Accuracy Test

The sensors of the motherboard were subjected to an accuracy test by comparing its data to commercially available devices. Settling the data acquired by the sensor and the commercially available device, a ±5% margin of error was used. The data gathering involves two ranges, the maximum range, and the minimum range. To automatically calculate the true or false validity of the data, the value of the sensors should be higher than the minimum range, and less than the maximum range. The pulse oximeter comparison involves one hundred data points for five persons each. Whereas, room temperature and ambient humidity comparison necessitate thirty-three data points for 8 rooms.

Figure 6. Accuracy Test for the Pulse Sensor and the Medical Device

Figure 7. Accuracy Test for the Room Temperature and the Ambient Humidity



Results and Discussion

Chart 1 compares the results of the motherboard sensor to the medical device. For each of the five subjects, a mean score was calculated using 100 data points. An accuracy test was done to ensure the quality of the device in measuring the beats per minute. Using the formula , where E represents the |𝐸−𝑇| |𝑇| × 100 experimental value and T as the theoretical value, the researchers calculated the percent error of each subject [5] . Subject 1 showed 3.36% margin of error; subject 2, 2.12% margin of error; subject 3, 1.26% margin of error; subject 4, 0.26% margin of error; and subject 5, 0.24% margin of error. The data collected complies with the ISO standard. The margin of error should be less than or equal to 5%, meaning that the device is applicable and performs as well as commercially available products.According to the American Heart Association, 90% of patients are unaware they are at high risk before a cardiac attack. Giving them medically calibrated equipment with real-time monitoring and wireless notification in case of an anomaly can reduce the risk [17] .

Chart 1.**Beats per Minute Data

The commercially available digital thermometer and Thermo hygrometer data were compared to the onboard HDC1080 sensor (labeled “motherboard sensor”) to calculate the temperature and humidity. The temperature and humidity were measured at Negros Prawn Consumers Cooperative (NPCC), an ISO-certified laboratory. As per international standards, this laboratory must maintain a constant temperature and humidity [18] . The same formula as beats per minute data was used to evaluate the device's temperature and humidity accuracy to the commercially available device [5] . Chart 2 shows the results of the temperature in 8 rooms inside the laboratory. In each room, a total of 32 data points for both the commercially available thermometer and the motherboard sensor were collected, and the mean of the data gathered was calculated. For each room, the percentage error of the device was used to determine its accuracy [5] . The temperature readings of the device in the Ante Room showed a 4.36% margin of error; Board Room, 0.35% margin of error; Biology Laboratory 1, 4.72% margin of error; Biology Laboratory 2, 1.95% margin of error; Biology Laboratory 3, 4.38% margin of error; PCR Section, 1.21% error; Stock Bioroom, 1.14% margin of error; and Microlaboratory with 4.54% margin of error.

Chart 2. Temperature Measured in degrees Celsius.

The humidity data collected in the nine rooms inside the laboratory is shown in Chart 3. The data from the onboard motherboard "HDC 1080" was compared to a commercially available Thermo hygrometer. The humidity data was obtained using the same method as the temperature data[5] , wherein 33 data points are recorded for both devices in each room and their mean calculated. The percentage error was calculated for each room to test the device’s accuracy in measuring the humidity [5] . For the Anteroom, the device observed a 2.68% margin of error; Board room, 4.77% margin of error; Biology Laboratory 1, 4.79% margin of error; Biology Laboratory 2, 2.28% margin of error; Biology Laboratory 3, 3.44% margin of error; PCR Section, 1.07% margin of error; Stock Bioroom, 4.50% margin of error; and lastly, Microlaboratory with 2.28% margin of error.

Chart 3. Humidity measured in percentage (%)

The IoT-Supported Smart Patient and Environment Monitoring System detect ambient temperature and humidity as accurately as a commercially available Thermo hygrometer. The device measures ambient temperature and humidity within the 5% margin of error [19] . It implies that the two devices are functionally equal, suggesting commercial viability [4][5]. In comparison, one study has found that some commercially available devices, such as Samsung and Apple, have a five or more percent margin of error [19]



Conclusion

Using the data presented, the researchers concluded that the IoT-Supported Smart Patient and Environment Monitoring System met the ISO standard for pulse oximeter accuracy. To provide safe and effective clinical treatment for patients and protect the health and safety of professional and lay device users, high-quality, well-designed medical devices are required [20] . The device is also accurate enough to compete with commercial Thermo hygrometers [4][5] .The study focuses on social innovation and suitable intervention measures to support persons with fragile independence and participation [21] . The device's sensors are smaller than prior systems[4] which improves usability and convenience. Design and ergonomics must be used as a strategic innovation tool to address human needs[21] .The Patient and Environment Monitoring system and commercial pulse oximeter are both efficient and accurate. It can already compete with a commercially marketed pulse oximeter. The device's main function, a miniature thermometer, also works with efficiency and reliability. With its inherent monitoring capabilities, the device can accurately and consistently measure ambient humidity. Making the sensor perform simultaneously in one innovation allows it to service many markets cost-effectively [4] . A high-integration sensor that is compact and rugged improves equipment portability [4] . Many obstacles relating to hardware providers, caregivers, regulatory restrictions, and technical qualities may hinder wide-scale adoption [22] . These features would enable intelligent, cost-effective data-driven care solutions to be delivered quickly. Moreover, the innovation's connectivity via IoT integration also allows healthcare anywhere and anytime [23].



Recommendation

The researchers recommend using the MAX30102 sensor to measure oxygen saturation levels in future research. A fast Internet connection is also required to send data from the ESP8266 module to the Firebase database. Optical signals detect pulse rate on the sensor board. Due to ambient lighting effects, the optical signals had to be calibrated. The algorithm itself isn't ideal, given that this library is open-source and only for developers. An optical waveform library should be included in future studies. This algorithm may not filter specific spikes. More precise measurements require changing the internal sensor register settings.



References

  1. Gardner, R. M., Clemmer, T. P., Evans, R. S., & Mark, R. G. (2014). Patient monitoring systems. In Biomedical Informatics (pp. 561-591). Springer, London.
  2. Smokoff, T. L., Marlin, T., & Uhrig, H. J. (1997). U.S. Patent No. 5,682,526. Washington, DC: U.S. Patent and Trademark Office.
  3. Drews, F. A. (2008). Patient monitors in critical care: Lessons for improvement. In Advances in Patient Safety: New Directions and Alternative Approaches (Vol. 3: Performance and Tools). Agency for Healthcare Research and Quality (US).
  4. Dias, D., & Paulo Silva Cunha, J. (2018). Wearable health devices—vital sign monitoring, systems, and technologies. Sensors, 18(8), 2414.
  5. Al-Naggar, N. Q., Al-Hammadi, H. M., Al-Fusail, A. M., & AL-Shaebi, Z. A. (2019). Design of a Remote Real-Time Monitoring System for Multiple Physiological Parameters Based on Smartphone. Journal of Healthcare Engineering, 2019.
  6. Di Rienzo, M., Rizzo, F., Parati, G., Brambilla, G., Ferratini, M., & Castiglioni, P. (2006, January). MagIC system: A new textile-based wearable device for biological signal monitoring. Applicability in daily life and clinical setting. In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference (pp. 7167-7169). IEEE.
  7. Kirtana, R. N., & Lokeswari, Y. V. (2017, January). An IoT-based remote HRV monitoring system for hypertensive patients. In 2017 International Conference on Computer, Communication and Signal Processing (ICCCSP) (pp. 1-6). IEEE.
  8. Tan, K. S., Saatchi, R., Elphick, H., & Burke, D. (2010, July). Real-time vision-based respiration monitoring system. In 2010 7th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 2010) (pp. 770-774). IEEE.
  9. Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012). A review of wearable sensors and systems with application in rehabilitation. Journal of neuroengineering and rehabilitation, 9(1), 21.
  10. Martin, T., Jovanov, E., & Raskovic, D. (2000, October). Issues in wearable computing for medical monitoring applications: a case study of a wearable ECG monitoring device. In Digest of Papers. Fourth International Symposium on Wearable Computers (pp. 43-49). IEEE.
  11. World Health Organization. Prevention of Cardiovascular Disease: Pocket guidelines for assessment and management of cardiovascular risk. Geneva, Switzerland: WHO; 2007.
  12. World Health Organization. Report of the World Health Organization Study Group. [Accessed December 15, 2009]; Diet, Nutrition and the Prevention of Chronic Diseases. 2008 Available from: http://whqlibdoc.who.int/trs/WHO_TRS_916.pdf.
  13. Willett, W. C., Koplan, J. P., Nugent, R., Dusenbury, C., Puska, P., & Gaziano, T. A. (2006). Prevention of chronic disease by means of diet and lifestyle changes. In Disease Control Priorities in Developing Countries. 2nd edition. The International Bank for Reconstruction and Development/The World Bank.
  14. Caudill, T. S., Lofgren, R., Jennings, C. D., & Karpf, M. (2011). Commentary: Health care reform and primary care: training physicians for tomorrow's challenges. Academic Medicine, 86(2), 158-160.
  15. US Department of Health and Human Services. (2004). Healthcare disparities in rural areas: Selected findings from the 2004 national health care disparities report. US Department of Health and Human Services, Agency for Healthcare Research and Quality.
  16. Levine, B. D. (2008). : what do we know, and what do we still need to know?. The Journal of Physiology, 586(1), 25-34.
  17. Wellens, H. J., Gorgels, A. P., & Munter, H. D. (2003). Cardiac Arrest Outside of a Hospital. Circulation, 107(15), 1948-1950.
  18. Maintaining Laboratory Temperature and Humidity to Retain the Validity of all Research. (2020, July 21).
  19. Phillips, C., Liaqat, D., Gabel, M., & de Lara, E. (2021, March). WristO2: Reliable peripheral oxygen saturation readings from wrist-worn pulse oximeters. In 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (pp. 623-629). IEEE.
  20. Martin, J. L., Norris, B. J., Murphy, E., & Crowe, J. A. (2008). Medical device development: The challenge for ergonomics. Applied Ergonomics, 39(3), 271-283.
  21. Tosi, F., Rinaldi, A., & Ricci, D. B. (2016). Ergonomics and Inclusive Design: Innovative Medical Devices for Home Care. In Advances in Design for Inclusion (pp. 401-412). Springer, Cham.
  22. Xing F., Peng G., Liang T., Jiang J. (2018) Challenges for Deploying IoT Wearable Medical Devices Among the Ageing Population. In: Streitz N., Konomi S. (eds) Distributed, Ambient and Pervasive Interactions: Understanding Humans. DAPI 2018. Lecture Notes in Computer Science, vol 10921. Springer, Cham. https://doi.org/10.1007/978-3-319-91125-0_25
  23. Dauwed, M., & Meri, A. (2019). IoT Service Utilisation in Healthcare. IoT and Smart Home Automation