6533b85afe1ef96bd12b8e9d
RESEARCH PRODUCT
Effect of Motion Artifact on Digital Camera Based Heart Rate Measurement
David FofiFabrice MeriaudeauNordin SaadMohamed Abul HassanAamir Saeed Maliksubject
Engineeringbusiness.product_category[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/ImagingRemote patient monitoringMotion artifact effect02 engineering and technology01 natural sciencesMotion (physics)Field (computer science)Photoplethysmography signalComputer vision[ SDV.IB ] Life Sciences [q-bio]/Bioengineering[ SDV.IB.IMA ] Life Sciences [q-bio]/Bioengineering/ImagingDigital cameraSkinPatient monitoringDigital cameraremote health monitoringCamerasBiomedical technologyHeart rate measurement[SDV.IB]Life Sciences [q-bio]/BioengineeringArtifactsAlgorithms[ INFO ] Computer Science [cs]0206 medical engineeringHeart rateCardiology[INFO] Computer Science [cs]010309 opticsMotionDatabases0103 physical sciences[INFO]Computer Science [cs]Heart rate measurement methodPhotoplethysmographyBiomedical measurementLighting[SDV.IB] Life Sciences [q-bio]/BioengineeringArtifact (error)Digital camerasMAHNOB-HCI databasebusiness.industry020601 biomedical engineeringMotion artifacts[SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/ImagingFace (geometry)FaceState (computer science)Artificial intelligencebusinessdescription
International audience; Depression is one of the most prevalent mental disorders, burdening many people world-wide. A system with the potential of serving as a decision support system is proposed, based on novel features extracted from facial expression geometry and speech, by interpreting non-verbal manifestations of depression. The proposed system has been tested both in gender independent and gender based modes, and with different fusion methods. The algorithms were evaluated for several combinations of parameters and classification schemes, on the dataset provided by the Audio/Visual Emotion Challenge of 2013 and 2014. The proposed framework achieved a precision of 94.8% for detecting persons achieving high scores on a self-report scale of depressive symptomatology. Optimal system performance was obtained using a nearest neighbour classifier on the decision fusion of geometrical features in the gender independent mode, and audio based features in the gender based mode; single visual and audio decisions were combined with the OR binary operation.
year | journal | country | edition | language |
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2017-07-11 |