A multi-context deep learning framework is established by applying deep learning CNN models trained to integrate relevant inputs, including rPPG signals, motion and robust luminance, to produce corresponding vital sign measurements.
100%
PROGRESS
A multi-context deep learning framework is established by applying deep learning CNN models trained to integrate relevant inputs, including rPPG signals, motion and robust luminance, to produce corresponding vital sign measurements.
FACE DETECTION
Using AI technology to detect human faces, even when faces are severely occluded.
MOTION & ROBUST LUMINANCE
Utilizing deep learning to adjust for motion and luminance variations.
rPPG SIGNAL EXTRACTION
Extracting blood volume changes and heartbeat waveforms by processing the subtle color fluctuations of the facial skin.
100%
PROGRESS
rPPG
TECHNOLOGY
Remote Photoplethysmography (rPPG) extracts subtle color changes under the human facial tissue through reflected ambient light captured by a normal camera. Signal amplification and noise management is applied on RGB (red, green, blue) channels to restore the original pulsatile signal.
TECHNICAL
SPECIFICATIONS
Camera
Front / Back Camera
FPS (Frames per Second)
15-30
Measurable State
Immobility / Fine Movement
Distance
0-500cm or Customized
Number of People
Single / Multiple People
Computation
Edge / Cloud Computing
Developing AI-powered technology for remote monitoring, FaceHeart has been recognized by the world’s leading innovation associations in the US, Europe and Asia.
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販賣業醫療器材商許可執照
販賣業醫療器材商許可執照
FH VITALS SDK TECHNOLOGY ADDS VALUE TO OUR CUSTOMERS' PRODUCTS
