TY - GEN
T1 - A Novel Real-Time Non-invasive Hemoglobin Level Detection Using Video Images from Smartphone Camera
AU - Ahsan, Golam M.T.
AU - Gani, Md O.
AU - Hasan, Md K.
AU - Ahamed, Sheikh I.
AU - Chu, William
AU - Adibuzzaman, Mohammad
AU - Field, Joshua
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/7
Y1 - 2017/9/7
N2 - Hemoglobin level detection is necessary for evaluating health condition in the human. In the laboratory setting, it is detected by shining light through a small volume of blood and using a colorimetric electronic particle counting algorithm. This invasive process requires time, blood specimens, laboratory equipment, and facilities. There are also many studies on non-invasive hemoglobin level detection. Existing solutions are expensive and require buying additional devices. In this paper, we present a smartphone-based non-invasive hemoglobin detection method. It uses the video images collected from the fingertip of a person. We hypothesized that there is a significant relation between the fingertip mini-video images and the hemoglobin level by laboratory 'gold standard.' We also discussed other non-invasive methods and compared with our model. Finally, we described our findings and discussed future works.
AB - Hemoglobin level detection is necessary for evaluating health condition in the human. In the laboratory setting, it is detected by shining light through a small volume of blood and using a colorimetric electronic particle counting algorithm. This invasive process requires time, blood specimens, laboratory equipment, and facilities. There are also many studies on non-invasive hemoglobin level detection. Existing solutions are expensive and require buying additional devices. In this paper, we present a smartphone-based non-invasive hemoglobin detection method. It uses the video images collected from the fingertip of a person. We hypothesized that there is a significant relation between the fingertip mini-video images and the hemoglobin level by laboratory 'gold standard.' We also discussed other non-invasive methods and compared with our model. Finally, we described our findings and discussed future works.
UR - http://www.scopus.com/inward/record.url?scp=85031913794&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85031913794&partnerID=8YFLogxK
U2 - 10.1109/COMPSAC.2017.29
DO - 10.1109/COMPSAC.2017.29
M3 - Conference contribution
AN - SCOPUS:85031913794
T3 - Proceedings - International Computer Software and Applications Conference
SP - 967
EP - 972
BT - Proceedings - 2017 IEEE 41st Annual Computer Software and Applications Conference, COMPSAC 2017
A2 - Demartini, Claudio
A2 - Conte, Thomas
A2 - Nakamura, Motonori
A2 - Lung, Chung-Horng
A2 - Zhang, Zhiyong
A2 - Hasan, Kamrul
A2 - Reisman, Sorel
A2 - Liu, Ling
A2 - Claycomb, William
A2 - Takakura, Hiroki
A2 - Yang, Ji-Jiang
A2 - Tovar, Edmundo
A2 - Cimato, Stelvio
A2 - Ahamed, Sheikh Iqbal
A2 - Akiyama, Toyokazu
PB - IEEE Computer Society
T2 - 41st IEEE Annual Computer Software and Applications Conference, COMPSAC 2017
Y2 - 4 July 2017 through 8 July 2017
ER -