Recognition of correct finger placement for photoplethysmographic imaging

Recognition of correct finger placement for photoplethysmographic imaging. Karlen, W., Lim, J., Ansermino, J., M., Dumont, G., A., & Scheffer, C. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 7480-3, 2013.

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In mobile health applications, non-expert users often perform the required medical measurements without supervision. Therefore, it is important that the mobile device guides them through the correct measurement process and automatically detects potential errors that could impact the readings. Camera oximetry provides a non-invasive measurement of heart rate and blood oxygen saturation using the camera of a mobile phone. We describe a novel method to automatically detect the correct finger placement on the camera lens for camera oximetry. Incorrect placement can cause optical shunt and if ignored, lead to low quality oximetry readings. The presented algorithm uses the spectral properties of the pixels to discriminate between correct and incorrect placements. Experimental results demonstrate high mean accuracy (99.06%), sensitivity (98.06%) and specificity (99.30%) with low variability. By sub-sampling pixels, the computational cost of classifying a frame has been reduced by more than three orders of magnitude. The algorithm has been integrated in a newly developed application called OxiCam where it provides real-time user feedback.

@inproceedings{
 title = {Recognition of correct finger placement for photoplethysmographic imaging},
 type = {inproceedings},
 year = {2013},
 keywords = {Consumer health,Emerging IT for efficient/low-cost healthcare deli,Mobile health,User experience,Wireless/ubiquitous technologies and systems},
 pages = {7480-3},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/24111475},
 city = {Osaka},
 id = {cc709576-791c-3538-b46e-63e7a3ad1a15},
 created = {2013-01-17T07:41:46.000Z},
 accessed = {2013-10-23},
 file_attached = {true},
 profile_id = {6d353feb-efe4-367e-84a2-0815eb9ca878},
 last_modified = {2022-09-04T18:11:57.818Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Karlen2013},
 folder_uuids = {439018bc-93d2-4d1e-9f95-7d8541a20cf1,f1f67efc-95a7-4f1a-b181-c3670c667a34,b7673acf-cf74-4294-9d7a-ff63418309e7,4afa922c-d8d6-102e-ac9a-0024e85ead87,6502c3f2-6e56-45fe-98d8-9274a086a27f,d9198259-8733-497d-ab87-d2a9518e0d30,283c5c0f-23cb-4595-855e-08632d168559},
 private_publication = {false},
 abstract = {In mobile health applications, non-expert users often perform the required medical measurements without supervision. Therefore, it is important that the mobile device guides them through the correct measurement process and automatically detects potential errors that could impact the readings. Camera oximetry provides a non-invasive measurement of heart rate and blood oxygen saturation using the camera of a mobile phone. We describe a novel method to automatically detect the correct finger placement on the camera lens for camera oximetry. Incorrect placement can cause optical shunt and if ignored, lead to low quality oximetry readings. The presented algorithm uses the spectral properties of the pixels to discriminate between correct and incorrect placements. Experimental results demonstrate high mean accuracy (99.06%), sensitivity (98.06%) and specificity (99.30%) with low variability. By sub-sampling pixels, the computational cost of classifying a frame has been reduced by more than three orders of magnitude. The algorithm has been integrated in a newly developed application called OxiCam where it provides real-time user feedback.},
 bibtype = {inproceedings},
 author = {Karlen, Walter and Lim, Joanne and Ansermino, J Mark and Dumont, Guy A and Scheffer, Cornie},
 doi = {10.1109/EMBC.2013.6611288},
 booktitle = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}
}

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