Examination of wrist radial pulse is a noninvasive diagnostic method, which occupies a very important position in Traditional Chinese Medicine. It is based on manual palpation and therefore relies largely on the practitioner′s subjective technical skills and judgment. Consequently, it lacks reliability and consistency, which limits practical applications in clinical medicine. Thus, quantifiable characterization of the wrist pulse diagnosis method is a prerequisite for its further development and widespread use. This paper reports application of a noninvasive CCD sensor-based hybrid measurement system for radial pulse signal analysis. First, artery wall deformations caused by the blood flow are calibrated with a laser triangulation displacement sensor, following by the measurement of the deformations with projection moiré method. Different input pressures and fluids of various viscosities are used in the assembled artificial blood flow system in order to test the performance of laser triangulation technique with detection sensitivity enhancement through microfabricated retroreflective optical element placed on a synthetic vascular graft. Subsequently, the applicability of double-exposure whole-field projection moiré technique for registration of blood flow pulses is considered: a computational model and representative example are provided, followed by in vitro experiment performed on a vascular graft with artificial skin atop, which validates the suitability of the technique for characterization of skin surface deformations caused by the radial pulsation.
[1]
M. Lehmann,et al.
Shape measurements on large surfaces by fringe projection
,
1999
.
[2]
Albert S. Kobayashi,et al.
Handbook on experimental mechanics
,
1987
.
[3]
Cyril Breque,et al.
Calibration of a system of projection moiré for relief measuring: biomechanical applications
,
2004
.
[4]
Anoop Lal Vyas,et al.
Radial pulse analysis at deep pressure in abnormal health conditions
,
2010,
2010 3rd International Conference on Biomedical Engineering and Informatics.
[5]
Hui-yan Wang,et al.
A model for automatic identification of human pulse signals
,
2008
.