Extracting Pulse Rate, Oxygen Saturation Level and Respiration Rate Through Smartphones
Name: LUCAS CÔGO LAMPIER
Publication date: 14/08/2024
Advisor:
Name |
Role |
|---|---|
| TEODIANO FREIRE BASTOS FILHO | Advisor |
Examining board:
| Name |
Role |
|---|---|
| ADRIANO DE OLIVEIRA ANDRADE | Examinador Externo |
| ALAN SILVA DA PAZ FLORIANO | Examinador Externo |
| PATRICK MARQUES CIARELLI | Examinador Interno |
| SRIDHAR KRISHNAN | Examinador Externo |
| TEODIANO FREIRE BASTOS FILHO | Presidente |
Pages
Summary: In the last years, the power of smartphones has been increasing. These devices, equipped
with multiple sensors and a high computational power, have become an essential part of
daily life. With their increasing capabilities, smartphones are no longer limited to basic
functions, but have emerged as versatile tools that can be utilized for multiple healthcare
purposes.
This work aims to use sensors that are built-in on smartphones, to extract human physio-
logical signals, as studies have shown that its color camera is capable to extract pulse rate
and oxygen saturation and its microphone can be used to measure respiration rate.
Multiple methods to measure pulse rate, oxygen saturation and respiration rate using
a color camera and a microphone are evaluated to be applied to the smartphone. New
methodologies based on Deep Learning (DL) to infer pulse rate and oxygen saturation of
people using a color camera are also presented, and a methodology to extract respiration
rate using a smartphone microphone is also evaluated.
It is shown that the DL models proposed are more accurate in measuring oxygen saturation
and pulse rate from small length signals than conventional methods proposed in the
literature. Using this model, the Root Mean Squared Error (RMSE) of the oxygen saturation
model was 2.92%, and the Spearman Rank Correlation Coefficient (SRCC) was 0.95. The
pulse rate was measured remotely and with the skin in contact with the camera. When
the skin is contact with the camera, the pulse rate RMSE was 1.78 BPM and an SRCC
of 0.96. When the pulse rate was measured remotely, the RMSE was 3.93 BPM and the
SRCC was 0.86. The respiration rate method also presented a low error, with RMSE of
0.74 breaths/min and a SRCC of 0.99.
Finally, a prototype of an Android application compiling the techniques to measure oxygen
saturation, pulse rate, and respiration rate was built. The application was tested with
eight volunteers, and the results showed that the pulse rate and respiration rate presented
low error, RMSE of 4.54 BPM and 0.74 breaths/min, respectively. However, the oxygen
saturation model did not perform well in the application (RMSE of 4.37 %), most likely
due to the differences between the setups used to record the model’s training images, and
to collect data using the application.
