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Esti mation of Respiratory Rate from Photoplethysmo graphic Imaging Videos Compared to Pulse Oximeter
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Esti mation of Respiratory Rate from Photoplethysmo graphic Imaging Videos Compared to Pulse Oximeter

Category : Android

Sub Category : bio-gedget

Project Code : ITANR09

Project Abstract

We present a study evaluating two respiratory rate estimation algorithms using videos obtained from placing a finger on the camera lens of a mobile phone. The two algorithms, based on Smart Fusion and Empirical Mode Decomposition (EMD), consist of previously developed signal processing methods to detect features and extract respiratory induced variations in photoplethysmographic signals to estimate respiratory rate. With custom-built software on an Android phone, photoplethysmographic imaging videos were recorded from 19 healthy adults while breathing spontaneously at respiratory rates between 6 to 32 breaths/min. Signals from two pulse oximeters were simultaneously recorded to compare the algorithms’ performance using mobile phone data and clinical data. Capnometry was recorded to obtain reference respiratory rates. Two hundred seventy-two recordings were analyzed. The Smart Fusion algorithm reported 39 recordings with insufficient respiratory information from the photoplethysmographic imaging data. Of the 232 remaining recordings, a root mean square error (RMSE) of 6 breaths/min was obtained. The RMSE for the pulse oximeter data was lower at 2.3 breaths/min. RMSE for the EMD method was higher throughout all data sources as, unlike the Smart Fusion, the EMD method did not screen for inconsistent results. The study showed that it is feasible to estimate respiratory rates by placing a finger on a mobile phone camera, but that it becomes increasingly challenging at respiratory rates greater than 20 breaths/min, independent of data source or algorithm tested.

Existing system

Proposed System

Ø  In the past decade, the doctor’s check every patient health one time per day.

Ø  Doctor manuallycheck the patient health condition and they must provide the treatment.




Ø  It is necessity to consult from doctor.

Ø  There is no automatic system.

Ø  It takes time to check manually.


        Here we introduce a smart phone to sense to recover the sensor information from patient body in waveform

      The system will update the patient health status day by day.




         It is an automatic system, so man power is not need.

      It saves the doctors time and it update prediction of patient health.


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