Future of Medicine: Health Monitoring with Mobile Phone Based Biomedical Sensors

1. What is biomedical sensor?


   Sensors are devices that detect specific physical, chemical or biological properties and convert them into signals for quantification [1]. Biomedical sensors gather information on body from various sources and convert the data into numerical value. 

   Biomedical sensors can be classified into physical sensors, chemical sensors and biosensors [2]. Physical sensors detect mechanical and thermal variables, which can be employed to measure blood pressure, body temperature or blood viscosity. Chemical sensors detect chemical quantities in the body and can be used in identifying presence or detecting the concentration of chemical composite such as pH value and glucose concentration. Biosensors detects biological features like hormone, DNA, antibody and enzyme.

   Receptors on humans and other animal species can also act as sensors. Human retina contains about 4.5 million cones and 91 million rods, and can see between 390 to 750 nm wavelengths in magnetic spectrum [9]. The surface area of the olfactory epithelium in average human is 10 cm2, which can detect molecules even in nM concentration [10].

   Humans used animals in healthcare, especially ones with superior olfactory system, as biosensors for diseases like tuberculosis or malaria. Dogs sense of smell is hundred thousand times more sensitive than humans, and can detect specific diseases from its odor.


                              Figure 1. Freya, the Springer Spaniel detecting scent of malaria from children’s socks [11]


   However, in order to be effectively used in future healthcare, a biosensor should be easily accessible, easy to use, and cost-effective. It should be a device that contain various sensors and yet as small as can be hold in a pocket.


2. Mobile phones and capabilities


   When its first introduced by Martin Cooper on April 3rd, 1973, the mobile phone was weighed nearly 1 Kg, had no internal memory, and functionally was limited to only make calls [3]. In 1992, IBM introduced Simon Personal Communicator, a cellular phone with a Personal Digital Assistant (PDA), which made the mobile phone a smartphone [4]. Modern day smartphones have evolved not just by their small size, but also with features like high memory capacity, high definition photography, music, sending and receiving emails, internet browsing etc. 

   Popularity of smartphones have also grown over in the past decade. In 2020, there are 3.5 billion smartphone users around the world and it is expected to exceed 3.8 billion in 2021 [5][6]. Even in the low-income countries, the mobile phone access is greater than 60% [7]. 

   Infectious and hematological diseases are most prevalent in areas with poor access to hospitals, laboratories, and trained personnel. Due to low resources and lack of health monitoring, diseases have devastating effects on these countries. Mobile phones therefore have a great potential to provide health monitoring service with easy and affordable price, particularly areas with limited access to healthcare facilities.


3. Mobile phone sensors and health monitoring


   Monitoring of physiological parameters and activities can be done by the sensors embedded in a mobile phone. Smartphones shares all the features with a common analytical reader. The screen of the phone act as both display and controller. Camera and light sensors can act as input for signal capture, and memory of the phone is used to store the health data.


           Figure 2. Sensors embedded in a modern day mobile phone (Majumder et al., 2019. Used with permission)


   Modern smartphones have various sensors such as a high-resolution complementary metal-oxide semiconductor (CMOS) image sensor, global positioning system (GPS) sensor, accelerometer, gyroscope, magnetometer, ambient light sensor and microphone [8]. With the help of these sensors, health parameters like heart rate, respiratory rate, physical activity and sleep cycle can be monitored.


             Table 1. Health issues monitored with smartphone sensors (Majumder et al., 2019. Used with permission)


4. Health monitoring with mobile phone biosensors


   As shown in Table 1, sensors fitted in mobile phone can detect several health parameters and allow long term monitoring of health data. Following sections breaks down several sensors to show advances in health monitoring by mobile phones.


    4.1 Optical based monitoring


   Camera is the one of the crucial features of a mobile phone. Image and videos captured by the camera can provide useful information about the patient, and combining with an application, these images can be used for doctor consolation or even diagnosis. Moreover, the camera feature can be used as a sensor for physiological features. A modern day HD camera can able to distinguish between 16777216 colors [12]. Camera can detect the primary colors (red, green, and blue (RGB)) and assigns numbers between 0-255. Small changes in the RGB code can be detected, which can be translated into physiological parameters like heart rate (HR) and heart rate variability (HRV).

   HR and HRV are both valuable indicators for cardiovascular health. HR is the number of times heart beats per minute. In healthy person it can range between 60-90 bpm and indicates how well the heart functioning. Monitoring HR can help detecting symptoms like irregular or rapid heartbeat (palpation), dizziness, fainting, chest pain, and shortness of breath [13]. Furthermore, it can also help diagnosis for tachycardia or bradycardia, which are the medical conditions where heart rate exceeds or under the normal range. Like HR, HRV can also indicates certain cardiovascular disorders. HRV is the time interval variation between heartbeats and has significant effect on sudden cardiac death, hypertension, and psychiatric disorders [14][15][16].

   Smartphone camera sensors can be used to obtain HR and HRV from photoplethysmogram (PPG). PPG is an optical way of measuring a changing volume, which captures the change in blood volume by illuminating the finger and measures the changes in skin illuminated light. Changes in the blood volume synchronous to heartbeat, therefore it can be used to measure heart rate and variability [17].


                                                                                                               Figure 3. Heart rate and heart rate variable detecting using smartphone. (a) placing finger on both camera and flash (Amended from Papon et al., 2015). (b)  HRV4 Training app showing heart rate and PPG.

When finger is placed on camera, application starts to capture frames while flash illuminates the finger. With every heartbeat, blood fills the capillaries and block the amount of light that can pass through. When it is retracted, more light can pass through. Change in the opacity can be detected by the camera in RGB values. With detection of RGB change, mobile phone generates PPG wave to show each heartbeat.

Another way of using mobile phone camera itself as biosensor is to detect harmful elements in a sample. Researchers were able to identify E.coli on spoiled meat by using the refractive index of the bacteria. With only using camera and light source, researchers able to identify bacteria with LOD 10 colony-forming-units per ml, which is lower than the infectious dose of E.coli (106-108) [18]

                                                     Figure 4. Detection of bacterium in spoiled meat with smartphone camera. Scatter detection made with 4 different angles; (a) 15o, (b) 30o, (c) 45o, and (d) 60o. (Amended from Liang et al., 2014)


   Mobile phones can also be coupled with simple 3D printed devices to be used as biosensors. With this method detection can be more precise due to distance between the camera and sample is fixed. It also provides a dark chamber for detection of fluorescent samples.

   One of the example is the albumin test that is installed on a mobile phone, which can measure and quantify albumin concentration in urine samples [19]. Albumin is a serum protein, which in normal conditions present at high concentrations in the blood. In cases like kidney damage, albumin can leak into urine. Therefore, detection of albumin in urine sample can imply kidney related diseases. Researchers build a compact add-on housing to a smartphone to integrate the phone with laser diode, two AA batteries, plastic lens, and emission interference filter to build a mobile albumin test, called Albumin Tester. The detection limit of Albumin Tester was 5-10µg/ml which is 3-fold lower than the clinical normal range of urinary albumin [19].

                                                                                         Figure 5. Albumin Tester installed on a mobile phone. (Amended from Coskun et al., 2013)


   Another application of mobile phone camera in biosensing is the microscopy. In most cases, microscopic analysis of biological samples requires expensive devices and trained personnel. Implementation of microscopy to mobile phones therefore can be very useful for diagnostics. With a simple 3D printed housing, a mobile phone can be used as a light microscope.

                                  Figure 6. Mobile phone microscopy. Lilium ovary analyzed with mobile phone (left) coupled with 3D printed apparatus. Camera flash is activated to view the sample in brightfield mode (Amended from Orth, et al. 2018). Stained blood sample is analyzed with same method (right) (Pfeil et al., 2017. Used with permission).


   Furthermore, images captured by the mobile phone microscope can be further analyzed by custom image recognition software that detects and quantifies biological samples. Therefore, mobile microscopic systems are valuable tools for diagnosis of patients especially in remote areas with poor medical supply.

    4.2 Microphone based monitoring

   Apart from the optical methods, microphones are also offer a sensitive biosensing feature in mobile phones. It is possible to obtain information about the respiratory health of a person by detecting respiratory sound (RS). Each pulmonary disorders have different characteristics of RS. Acoustic based mobile phone biosensors therefore provide easy to use bedside detection of pulmonary diseases.

   One of the examples is the detection of crackle sound with a mobile phone coupled with an acoustic sensor [22]. Crackle sound is considered to imply a pathological process in pulmonary airways. Timing of the crackle sound was also shown that reflects different pulmonary disorders.


                Figure 7. Mobile phone based acoustic biosensor (left) and acquisition at bedside of a patient diagnosed with                                                               pneumonia (right) (Reyes et al., 2018. Used with permission).


   Another use of mobile phone microphone is measurement of lung capacity with spirometer. Spirometry is the measure of lung function, where the patient forcefully exhales through a flow-monitoring device that measures instantaneous flow and cumulative exhaled volume [23]. Spirometry can give valuable data for diagnosis of chronic lung diseases like asthma, cystic fibrosis, and chronic obstructive pulmonary disease (COPD).


                                                                              Figure 8. Blowing air into microphone to use mobile phone as a spirometer (left) example curves from the                                                         SpiroSmart application (right) (Amended from Larson et al., 2012).


4.3 Accelerometer, gyroscope, magnetometer, and GPS based monitoring


   Physical activity has significant health benefits and can prevents certain diseases. Physical activity can be defined by the general movement of the body by the skeletal muscle that requires energy expenditure [24]. Activities done during leisure time or transport like walking, running, and climbing stairs are considered as physical activities. Insufficient activity is one of the leading risk factors worldwide and can lead to variety of chronic diseases such as obesity, diabetes, and cardiovascular diseases.

   On the other hand, some diseases can affect the physical activities by affecting musculoskeletal or nervous system. In early onset of the Parkinson’s disease, patients experience difficulties in walking. Therefore, long term monitoring of daily physical activities can be useful not just for well-being, but also early detection for diseases that affects motor capabilities.

   Sensor embedded in a mobile phone like accelerometer, GPS, magnetometer and gyroscope can identify the movements done by the user. Data gathered from the combination of sensors usually provides more accurate activity recognition. For example, combination of accelerometer data with barometer data provides a better accuracy in distinguishing walking up and down the stairs [25]. Based on the activity detected, mobile phone applications can also calculate the total number of calories burned during the activity or in a day.


                                                                                                                   Figure 9. Fitbit application dashboard with activity data


5. Conclusion and future perspectives


   Advances in the sensor and mobile phone technologies has enabled integration of more sensors into modern day smartphones. Health data gathered from these sensors can be used in diagnostics of cardiovascular diseases, pulmonary diseases, skin diseases, hematological diseases and many more. These diseases can be detected with higher accuracy and sensitivity with biomedical sensors in hospitals and laboratories however, today, mobile phones provide much more accessibility and cost effective way of monitor health without need of a trained personnel.

   Non-invasive and easy to use mobile phone sensors also provides long term health monitoring of the user, without interacting the daily routines or adding extra personal expenses. While users living their normal lives, mobile phones can keep monitoring the health data. Monitoring of daily activities in a continuous fashion therefore can provide a detailed information on overall health of the user. With artificial intelligence, overall health data play an incredible role in estimation and early diagnosis of diseases.

   In future, with advances in the mobile phone technology, more sensitive sensors will be implemented with smartphones. With increased number of sensors, more physiological parameters can be monitored and more abnormalities can be detected. With more diverse biosensors, accuracy of the diagnosis will also be much higher than todays mobile phones.




  1. National Institute of Biomedical Imaging and Bioengineering (NIBIB). Sensors. Available online: https://www.nibib.nih.gov/science-education/science-topics/sensors
  2. Zhou, G., Wang, Y., & Cui, L. (2015). Biomedical sensor, device and measurement systems. Advances in Bioengineering177.
  3. Kennedy, P., 2013, march 15, The New York Times, Who Made That Cellphone?
  4. Tweedie, S. The World’s First Smartphone, Simon,Was Created 15 Years Before the iPhone. Business Insider. 14 June 2015.
  5. Deyan, G. 67+ Revealing Smartphone Statistics for 2020. 15 October 2020 Available online: https://techjury.net/blog/smartphone-usage-statistics/#gref
  6. Kooistra, J. Newzoo’s 2018 Global Mobile Market Report: Insights into the World’s 3 Billion Smartphone Users. Newzoo. 11 September 2018 

  7. Bastawrous, A., & Armstrong, M. J. (2013). Mobile health use in low-and high-income countries: an overview of the peer-reviewed literature. Journal of the royal society of medicine106(4), 130-142.
  8. Majumder, S., & Deen, M. J. (2019). Smartphone sensors for health monitoring and diagnosis. Sensors19(9), 2164.
  9. Purves D, Augustine GJ, Fitzpatrick D, et al., editors. Neuroscience. 2nd edition. Sunderland (MA): Sinauer Associates; 2001. Anatomical Distribution of Rods and Cones.Available from: https://www.ncbi.nlm.nih.gov/books/NBK10848/
  10. Purves D, Augustine GJ, Fitzpatrick D, et al., editors. Neuroscience. 2nd edition. Sunderland (MA): Sinauer Associates; 2001. Olfactory Perception in Humans.Available from: https://www.ncbi.nlm.nih.gov/books/NBK11032/
  11. Jo Khan, ABC Health & Well Being, Dogs sniffing out human disease could lead to new biosensor technology, 29 Oct 2018, Available online: https://www.abc.net.au/news/health/2018-10-30/medical-detection-dogs-disease-sensors/10425488
  12. Quesada-González, D., & Merkoçi, A. (2017). Mobile phone-based biosensing: An emerging “diagnostic and communication” technology. Biosensors and Bioelectronics92, 549-562.
  13. Zhou, G., Wang, Y., & Cui, L. (2015). Biomedical sensor, device and measurement systems. Advances in Bioengineering177.
  14. Lammers AE, Kaemmerer H, Hollweck R, Schneider R, Barthel P., “Heart rate turbulence and heart rate variability predict sudden cardiac death in patients with congenital heart disease.” J Am Coll Cardiol, 2005;45:320A.
  15. Abramkin DV, Yavelov IS, Gratsiansky NA. “Relationship between heart rate changes during reflex tests and heart rate variability in patients with recent myocardial infarction.” Kardiologiya 2004;44:2734.
  16. Madsen LB,Moller DS, Rasmussen JK, Nyvad O, Pedersen EB. “Abnormal heart rate variability in essential hypertension. J Hypertens 2005;23:S382.
  17. Papon, M. T. I., Ahmad, I., Saquib, N., & Rahman, A. (2015, January). Non-invasive heart rate measuring smartphone applications using on-board cameras: A short survey. In 2015 International Conference on Networking Systems and Security (NSysS)(pp. 1-6). IEEE.
  18. Liang, P. S., San Park, T., & Yoon, J. Y. (2014). Rapid and reagentless detection of microbial contamination within meat utilizing a smartphone-based biosensor. Scientific reports4(1), 1-8.
  19. Coskun, A. F., Nagi, R., Sadeghi, K., Phillips, S., & Ozcan, A. (2013). Albumin testing in urine using a smart-phone. Lab on a Chip13(21), 4231-4238.
  20. Orth, A., Wilson, E.R., Thompson, J.G. et al.A dual-mode mobile phone microscope using the onboard camera flash and ambient light. Sci Rep 8, 3298 (2018). https://doi.org/10.1038/s41598-018-21543-2
  21. Pfeil, J., Frohme, M., & Schulze, K. (2017). Mobile microscopy for the examination of blood samples.  journal23, 904.
  22. Reyes, B. A., Olvera-Montes, N., Charleston-Villalobos, S., González-Camarena, R., Mejía-Ávila, M., & Aljama-Corrales, T. (2018). A Smartphone-Based System for Automated Bedside Detection of Crackle Sounds in Diffuse Interstitial Pneumonia Patients. Sensors18(11), 3813.
  23. Larson, E. C., Goel, M., Boriello, G., Heltshe, S., Rosenfeld, M., & Patel, S. N. (2012, September). SpiroSmart: using a microphone to measure lung function on a mobile phone. In Proceedings of the 2012 ACM conference on ubiquitous computing(pp. 280-289).
  24. World Health Organization (WHO), Physical Activity, 23 Feb 2018, Available online: https://www.who.int/news-room/fact-sheets/detail/physical-activity
  25. Cruciani, F., Cleland, I., Nugent, C., McCullagh, P., Synnes, K., & Hallberg, J. (2018). Automatic annotation for human activity recognition in free living using a smartphone. Sensors18(7), 2203. 
  26. Featured Image From: https://www.medicaleconomics.com/view/how-mobile-tech-transforming-healthcare


Arman Dalay


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