1945-7111/167/13/137510
Abstract
There is a significant interest in the analysis of biphasic temperature pattern for natural family planning (NFP). In this paper, we report an IoT enabled Basal Body Temperature (BBT) monitoring system—designed and developed using a NiMn2O4 nanocomposite powder based thermistor. BBT is the lowest body temperature attained at rest and normally ranges from 308.70 to 310.65 K. The pelletized active material NiMn2O4 was placed in a thermally conductive Teflon tube with Aluminum (Al) metal contacts on either side as electrical contacts to the active material. Resistance of the Al/NiMn2O4/Al composite structure is inversely proportional to temperature changes. A resistance change of 953000 ± 500 to 591000 ± 450 Ω was observed for a change of basal body temperature from 304.65 to 313.15 K with a correlation coefficient (R2) of 0.9628. Real time monitoring of BBT was facilitated by Node MCU and ThingSpeak IoT platform for predicting the ovulation pattern in women.
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Temperature is one of the most often measured environmental parameter as most of the physical, chemical, biological, mechanical and electronic systems exhibit some form of temperature dependence.1 Human body temperature is a complex, non-linear variable, internally and externally influenced by various factors such as age, exertion, infection, gender, location in the body at which the measurement is made, time of day when the measurement is taken, the state of consciousness of the subject, activity level, emotional state, psychological factors, insufficient and shift in the schedule of sleep, and reproductive status of the subject.2–4 Body temperature has been termed as an expression of molecular excitation.5 Widely accepted normal temperature for a healthy adult is approximately 98.6 °F and normal temperature of a human is generally varies from 97.7 °F to 99.5 °F.6 Temperature is measured with various types of thermometers such as analog clinical thermometer, digital thermometer, tympanic ear thermometer, and probes thermometer, etc. Locations at which human body temperature is measured are rectum (rectal temperature), mouth (oral temperature), under the arm (auxiliary temperature), ear (tympanic temperature), nose, vagina, bladder, on the skin of the forehead over the temporal artery.7,8 Due to various bioprocesses and functions, temperature of body has predictable time-sensitive variations termed as biological rhythms, at every level of organization has been validated.9,10 Body temperature fluctuates over the day with the highest in the late afternoon, between 4:00 and 6:00 PM and the lowest levels around 4.00 AM.8,11 The lowest temperature attained usually when the body is at rest is referred as Basal Body Temperature (BBT).12
During the follicular phase of the menstrual cycle, BBT reaches its lowest point approximately 1 d before ovulation due to higher levels of estrogen, after ovulation, higher levels of progesterone released by the corpus luteum BBT rises sharply after ovulation to about 0.5 to 1 °C throughout the luteal phase, this phenomenon is termed as Biphasic Temperature Pattern.13–15 NiMn2O4 ceramic material has a negative temperature coefficient (NTC) of resistivity and a broad spectral response range due to its Mn–Ni–O spinel-structure which has found applications in thermal sensitive device applications.16,17 Charting of biphasic temperature pattern of BBT is used as a component of fertility awareness method of natural family planning in symptothermal method which is a safe and non-invasive method to detect ovulation by tracking the signs and indications of the body.18,19 The real time accurate estimation of ovulation, as an indicator, helps to achieve or avoid pregnancy naturally.12,20,21
In this work, IoT enabled BBT monitoring system is designed using a NiMn2O4 nanocomposite powder synthesized by modified wet chemical method. The sensing system was employed to monitor the biphasic temperature pattern of BBT graph that can predict the ovulation period in women.
Experimental and Methods
Chemicals
Nickel acetate tetra hydrate (C4H14NiO8), Manganese acetate tetra hydrate ((CH3COO) 2Mn · 4H2O), Oxalic acid (C2H2O4) and HNO3 are the precursor materials used to prepare the Al/NiMn2O4/Al active layers of the thermistor. All the precursor materials were purchased from Finar India.
Device characterization
Surface morphology of the NiMn2O4 nanocomposite powder is characterized using field emission scanning electron microscopy (FeSEM) with energy-dispersive X-ray spectroscopy (EDS). The crystallinity nature of NiMn2O4 was examined using X-ray diffractometer (XRD) with peak intensities were in the range of 10° to 80° at 2θ degree angle in scanning rate. The electrical performance of the Al/NiMn2O4/Al structure was measured using a digital multimeter. All the data measurements were repeated and analyzed using three similar sets of fabricated Al/NiMn2O4/Al structure. Homemade heater, LM35 temperature sensor, NTC thermistor, Node MCU ESP8266, DC power supply and ThingSpeak IoT software platform used to develop the real time BBT monitoring system.
Fabrication of NiMn2O4 nanocomposite powder-based thermistor
Nanocomposite powder of NiMn2O4 was synthesized modified a wet chemical synthesis method. Nickel acetate tetra hydrate (C4H14NiO8) and Manganese acetate tetra hydrate ((CH3COO)2 Mn·4H2O) were mixed in the 1:2 ratio and dissolved in the distilled water. The resultant solution was heated at 40 °C for 30 min. Oxalic acid (C2H2O4) solution was further added to the tetrahydrate solution under vigorous stirring. The obtained thick composite was dried at 100 °C and grounded using pestle and mortar. Further nanocomposite powder was calcined at 1000 °C for 3.5 h to obtain the NiMn2O4 nanocomposite powder which was used in the fabrication of the thermistor.
The active NiMn2O4 nanocomposite powder is well pressed and stored in a thermally conducting Teflon tube. Aluminum (Al) metal electrodes were inserted into the two open sides of the Teflon tube to build an electrical connection between the active material and electrodes. Figure 1 depicts the schematic illustration of NiMn2O4 nanocomposite powder based thermistor fabrication process. The thermistor was calibrated by recording resistance data proportional to temperature ranging from 304.65 to 313.15 K at a response time of 2 min. The fabricated temperature sensor was integrated into a Node MCU and calibrated using the β-coefficient thermistor model. The resulting temperature sensor system was connected with ThingSpeak IoT platform to measure real time biphasic temperature pattern for ovulation predications.
Results and Discussion
Characterizations of NiMn2O4 nanocomposite powder
The surface morphology and chemical composition of the NiMn2O4 nanocomposite powder (Figs. 2 and 3) were separately examined using field emission scanning electron microscopy (FeSEM) and energy dispersive spectrometer (EDS). The majority of the NiMn2O4 nanocomposite powder (Fig. 2) displayed high regularity in surface morphology with well rounded (spherical) nanocomposite grains.
The EDS composition spectrum (Fig. 3) confirms the presence of Nickel (Ni) Manganese (Mn) and Oxygen (O) elements in the NiMn2O4 nanocomposite powder and no other impurity was detected in the chemical compositional studies. In addition, the result also indicates the higher distribution of the Manganese (Mn) and Nickel (Ni) in the scan area.
Further, NiMn2O4 nanocomposite powders were characterized using X-ray diffraction (XRD) technique (Fig. 4). The peak intensities were measured in the range of 10° to 80° at a 2θ degree angle in the scanning rate. There are 9 peaks observed in this measurement of which the maximum peak (1377 cps) is observed at 35.82° with 0.31° FWHM whereas the smallest peak (356 cps) is observed at 53.76° with 0.34° FWHM. Scherer formula in Eq. 1 is used to determine the average grain size of the NiMn2O4 nanocomposite powder.
Where λ is the X-ray wavelength of CuKα source 0.154059 nm, θ is the Bragg's angle and β is the full width at half maximum (FWHM) of the diffraction peak in radians. The average crystallite size D of the resulting powder found to be ≈ 26.62 nm and standard deviation (σ) of 1.92.
Design and development of NiMn2O4 thermistor based BBT monitoring system
In order to evaluate the NTC performance of the NiMn2O4 nanocomposite powder, resistance measurements were performed by introducing the temperature module using a homemade heater system. Resistance—Temperature characteristics of the NiMn2O4 nanocomposite powder were studied with increasing Temperature level 304.65 to 313.15 K. The performance was projected by plotting the graph of Temperature (K) vs Resistance (Ω) (Fig. 5). The resistance was found to linearly decrease from 953000 ± 500 to 591000 ± 450 Ω range for temperature from 304.65 to 313.15 K with a correlation coefficient (R2) of 0.9628.
The β coefficient model is used to develop the NiMn2O4 nanocomposite based thermistor. Device modeling parameters such as room temperature resistance (R0), room temperature (T0) and β-coefficient are calculated using online SRS thermistor calculator software. The β-coefficient model becomes
The calculated β-parameter equation model confirms the room temperature resistance (R0) = 1509613.24Ω, room temperature (T0) = 298.15 K and β-coefficient (β) = 642798 K. The change in 1/T(K) vs ln(R) of the β-coefficient model parameter for the thermistor 1 is shown in Fig. 6. The resulting thermistor model offers a precision of more than three-digit and can measure the temperature in the range of 25 °C–50 °C.
The error difference between the fabricated thermistor I, thermistor II and thermistor III models are 0.6% and 0.8%, which is an acceptable error margin of less than <1%. These results show that thermistor models of I, II and III possess good stability (Fig. 7). To evaluate the sensor performance, the body temperature of the three healthy volunteers was measured using the fabricated NiMn2O4 thermistor. The obtained results were compared with the commercially available LM 35 IC and NTC thermistor results with identical condition. Both the readings are well-matched and the maximum error was found to be 0.042 °C. The distinctive future of this thermistor is the simplicity of construction and 0.01/0.001 resolution. Calibration system does not require any complex circuitry and techniques to fabricate it.
The embedded BBT monitoring system is developed on a NodeMCU ESP8266 platform. Further Wheatstone bridge signal conditioning circuit is integrated into the fabricated NiMn2O4 thermistor to convert the obtained thermistor resistance into a microcontroller readable signal. A 1.08 MΩ resistor is associated in series with the fabricated NiMn2O4 thermistor as a voltage divider. Thermistor acquired data is conditioned using a thermistor circuit to provide the appropriate signal voltage to the NodeMCU ESP8266. In order to incorporate Internet of Things (IoT) to the developed BBT monitoring system, ThingSpeak open-source IoT platform was integrated and software programme written in Arduino IDE.
Figure 8 shows the schematic illustration of developed IoT enabled embedded system for real-time BBT monitoring. The BBT readings from the device is taken in the morning by 5 AM every day on the alarm basis provided in the smart BBT monitoring belt system. Further daily measured BBT data uploaded to ThingSpeak channel called "BBT monitoring" is created by the user. API read and write keys are auto-generated to the user account and was used in software programming. The process of uploading sensor values to ThingSpeak cloud integration using Arduino IDE process flow provided in the supplementary materials. ThingSpeak platform performs temperature vs day of BBT graph to provide the graphical visualization of biphasic pattern of women BBT.
The integration of cost-effective NiMn2O4 thermistors and wireless data transmitter enabled microcontroller platform in a single system was impressively reduced the overall cost of the developed BBT monitoring device. The total device development cost is around 7 USD and it will reduce during the bulk fabrication process. Figure 9 shows the measured BBT temperature vs day of the women 1 and 2 for the duration from 2nd to 21st November 2019 and 2nd to 21st December 2019 respectively.
It can be noticed that BBT is low before ovulation and high after the ovulation, by analyzing this biphasic pattern of BBT graph Fig. 9. The BBT pattern drastically rise to 98.3 °F from the drop of 97 °F between the 11th to 14th day of the measured cycle for women 1 and a drastic rise to 99.2 °F from the drop of 98.1 °F happened for women 2. So the occurrence of ovulation can be predicted likely to be on 11 h − 14th of day of the measured month for women 1 and women 2 respectively.
Conclusions
Herein we report the IoT enabled BBT monitoring system is designed and developed using a NiMn2O4 nanocomposite based thermistor. NiMn2O4 nanocomposite powder was synthesized by a modified wet chemical method. Further, Aluminum (Al) electrodes were inserted into an electrical insulator but thermally conductor Teflon tube to build an electrical contact between the active material and electrodes. The resulting composite structure Al/NiMn2O4/Al was found to have a proportional resistance change with temperature. The resistance was found to linearly decrease from 953000 ± 500 to 591000 ± 450 Ω range for temperature from 304.65 to 313.15 K with a correlation coefficient (R2) of 0.9628. Further, Node MCU based BBT monitoring device is developed with the help of ThingSpeak IoT platform to facilitate easy transmission, storage, processing and plot of data in real-time. The developed device successfully measured the basal body temperature with precision more than three digits, which is more convenient to analysis of biphasic temperature pattern of BBT graph to predict the ovulation period in women. In addition, the developed embedded system facilitates with the IoT in order to have the interventions of specialists for the accurate detection of ovulation period in real-time. So that the symptothermal method of estimating ovulation in women is more accurate and enhance the efficiency and effectiveness of natural family planning (NFP).
