Raksha - Vitals Monitor for Frontline Workers

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Introduction: Raksha - Vitals Monitor for Frontline Workers

About: Electronics engineer who loves to experiment new things.

Wearable health monitoring technologies, including smartwatches and fitness trackers, have attracted considerable consumer interest over the past few years. Not only has this interest has been mainly encouraged by the rapid demand growth in the wearable technology market for the ubiquitous, continuous, and pervasive monitoring of vital signs, but it has been leveraged by the state-of-the-art technological developments in sensor technology and wireless communications. The wearable technology market was valued at over $13.2 billion by the end of 2016 and its value is forecast to reach $34 billion by the end of 2020.

There are many sensors for measuring vitals of the human body which are essential for a doctor or a medic to know the health problems. We all know that doctor first checks Heart Rate to know Heart Rate Variability (HRV) and body temperature. But the current wearable bands and devices fail in the accuracy and repeatability of the measured data. This mostly happens due to miss alignment of fitness tracker and erroneous reading etc. Most use the LED and Photodiode based Photo Plethysmography (PPG) sensors for the heart rate measurement.

Features:

  • Battery-powered wearable
  • Measures real-time heart rate and inter-beat Interval (IBI)
  • Measures real-time body temperature
  • Plots real-time graph on the display
  • Sends data over Bluetooth to mobile phone
  • Data can be recorded and sent to the doctor directly for further analysis.
  • Good battery management with included sleep.
  • By sending the data to the cloud it creates a huge database for researchers working on medical solutions to COVID-19.

Supplies

Hardware Needed:
  • SparkFun Arduino Pro Mini 328 - 5V/16MHz×1
  • pulse sensor×1
  • thermistor 10k×1
  • Rechargeable Battery, 3.7 V×1
  • HC-05 Bluetooth Module×1

Software apps and online services

  • Arduino IDE

Hand tools and fabrication machines

  • 3D Printer (generic)
  • Soldering iron (generic)

Step 1: Let's Start

Currently, modern wearable devices are no longer only focused on simple fitness tracking measurements such as the number of steps taken in a day, they also monitor important physiological considerations, such as Heart Rate Variability (HRV), glucose measures, blood pressure readings, and much additional health-related information. Among the numerous vital signs measured, the heart rate (HR) calculation has been one of the most valuable parameters. For many years, file Electrocardiogram (ECG) has been used as a dominant cardiac monitoring technique to identify cardiovascular abnormalities and to detect irregularities in heart rhythms. The ECG is a recording of the electrical activity of the heart. It shows the variations in the amplitude of the ECG signal versus time. This recorded electrical activity originates from the depolarization of the conductive pathway of the heart and the cardiac muscle tissues during each cardiac cycle. Even though traditional cardiac monitoring technologies using the ECG signals has undergone continuous improvements for decades to address the ever-changing requirements of their users, specifically in terms of measurement accuracy.

These techniques, up to now, have not been enhanced to the point of offering the user flexibility, portability, and convenience. For instance, for the ECG to operate effectively, several bio-electrodes must be placed at certain body locations; this procedure greatly limits the moving flexibility and mobility of the users. In addition, PPG has shown itself to be an alternative HR monitoring technique. By using detailed signal analysis, the PPG signal offers excellent potential to replace ECG recordings for the extraction of HRV signals, especially in monitoring healthy individuals. Therefore, to overcome the ECG limitations, an alternative solution based on PPG technology can be used. By all these data we can conclude that measuring the heart rate and body temperature and analysing them to know to check whether there is abnormal body temperature raises and lower SpO2 oxygen levels in haemoglobin will help in early detection of COVID-19. Since this device is a wearable this can help front line workers such as doctors, nurses, police officers and sanitation workers who are doing day and night service to fight against COVID-19.

Get the required parts we can change displays and sensor type based on the requirement. There is one more good sensor MAX30100 or MAX30102 for the heart rate measurement using PPG technique.
I'm using a 10k thermistor for temperature measurement, one can use any temperature sensor such as LM35 or DS1280 etc

Step 2: Designing the Case

In order to wear a wearable gadget, it should be enclosed in a proper case in order to protect from damages, so I went ahead and designed a case which can fit all my sensors and MCUs.

Step 3: Assembling Electronics

Now we need to connect all the required components, earlier I had a plan of choosing ESP12E as MCU but since it has only one 1 ADC pin and I wanted to interface 2 analog devices I reverted back to Arduino with a Bluetooth configuration.

I almost chose ESP 12E

With ESP one can directly send the data to the cloud may be a personal server or website like thingspeak and shared directly to the concerned personnel from there.

Schematic

The earlier cable-based connection had a lot of issues with wire getting broken due to twist and turn in constrained space, later I moved to insulated copper wire from the armature of a DC motor. Which is pretty robust I should say.

Step 4: Coding

The basic idea is like this.

The working principle of PPG sensors is basically by illuminating Light on the fingertip and measuring the intensity of light by using photo-diode. Here I'm using of the shelf pulse sensor from www.pulsesensor.com. I have mentioned other alternatives in the parts section. We will measure the analog voltage variation at the analog pin 0 which is, in turn, a measurement of blood flow at the fingertip or at the wrist by which we can measure the heart-rate and the IBI.
For temperature measurement we are using a 10k NTC thermistor, mine is extracted from a laptop battery pack. Here, an NTC type thermistor of 10kΩ is used. NTC of 10kΩ means that this thermistor has a resistance of 10kΩ at 25°C. The voltage across the 10kΩ resistor is given to the ADC of pro-mini-board.

The temperature can be found out from thermistor resistance using the Steinhart-Hart equation.
Temperature in Kelvin = 1 / (A + B[ln(R)] + C[ln(R)]^3) where A = 0.001129148, B = 0.000234125 and C = 8.76741*10^-8 and R is the thermistor resistance. Note that log() function in Arduino is actually a natural log.

int thermistor_adc_val;

double output_voltage, thermistor_resistance, therm_res_ln, temperature, tempf; thermistor_adc_val = analogRead(thermistor_output);

output_voltage = ( (thermistor_adc_val * 3.301) / 1023.0 );

thermistor_resistance = ( ( 3.301 * ( 10 / output_voltage ) ) - 10 );

/* Resistance in kilo ohms */

thermistor_resistance = thermistor_resistance * 1000 ;

/* Resistance in ohms */

therm_res_ln = log(thermistor_resistance);

/* Steinhart-Hart Thermistor Equation: */ /* Temperature in Kelvin = 1 / (A + B[ln(R)] + C[ln(R)]^3) */ /* where A = 0.001129148, B = 0.000234125 and C = 8.76741*10^-8 */ temperature = ( 1 / ( 0.001129148 + ( 0.000234125 * therm_res_ln ) + ( 0.0000000876741 * therm_res_ln * therm_res_ln * therm_res_ln ) ) ); /* Temperature in Kelvin */ temperature = temperature - 273.15; /* Temperature in degree Celsius */

Serial.print("Temperature in degree Celsius = ");

Serial.println(temperature);

Complete code can be found here.

Step 5: Testing and Working.

Step 6: Future Enhancements and Conclusion

Future enhancements:

  • I would like to add the following features:
  • Using Tiny ML and Tensorflow lite to detect the anomaly.
  • Optimizing battery by using BLE
  • Android application for the personalised notifications and suggestions regarding the health
  • Adding a vibration motor for alerting

Conclusion:

With the help of opensource sensors and electronics, we can really make changes in the lives of frontline workers by detecting the COVID-19 symptoms i.e, variation in HRV and Body temperature one can detect the changes and suggest them to get quarantined to stop the spread of the disease. The best part of this device is, it is under 15$ which is a lot cheaper than any available fitness tracker etc.and hence government can make these and protect the front line workers.

Arduino Contest 2020

Participated in the
Arduino Contest 2020

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    2 Comments

    0
    Mj3x
    Mj3x

    1 year ago

    hello
    Very nice.
    thanks

    0
    Aaron Sabu
    Aaron Sabu

    1 year ago

    Its an Apple Watch but with Arduino!