Extracting pulse rate, oxygen saturation, and respiration rate through smartphones
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Data
2024-08-14
Autores
Lampier, Lucas Côgo
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Universidade Federal do Espírito Santo
Resumo
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 cameras to extract pulse rate and oxygen saturation, and microphones 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 these 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
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Aprendizado profundo , Saturação de oxigênio , Frequência cardíaca , Frequência respiratória , Smartphone , Deep learning , Oxygen saturation , Pulse rate , Respiration rate