In order to evaluate the safety performance of self-expandable NiTi alloy stents systematically, the dynamic safety factor drawn up by International Organization for Standardization, was used to quantitatively reflect the safety performance of stents. Based on the constitutive model of super-elastic memory alloy material in Abaqus and uniaxial tensile test data of NiTi alloy tube, finite element method and experiments on accelerated fatigue life were carried out to simulate the self-expansion process and the shape change process under the action of high and low blood pressure for three L-type stents of Φ8×30 mm, Φ10×30 mm, Φ12×30 mm. By analyzing the changes of stress and strain of self-expanding NiTi alloy stent, the maximum stress and strain, stress concentration position, fatigue strength and possible failure modes were studied, thus the dynamic safety factor of stent was calculated. The results showed that the maximum stress and plastic strain of the stent increased with the increase of grip pressure, but the maximum stress and strain distribution area of the stent had no significant change, which were all concentrated in the inner arc between the support and the connector. The dynamic safety factors of the three stents were 1.31, 1.23 and 1.14, respectively, which indicates that the three stents have better safety and reliability, and can meet the fatigue life requirements of more than 10 years, and safety performance of the three stents decreases with the increase of stent’s original diameter.
In order to solve the saturation distortion phenomenon of R component in fingertip video image, this paper proposes an iterative threshold segmentation algorithm, which adaptively generates the region to be detected for the R component, and extracts the human pulse signal by calculating the gray mean value of the region to be detected. The original pulse signal has baseline drift and high frequency noise. Combining with the characteristics of pulse signal, a zero phase digital filter is designed to filter out noise interference. Fingertip video images are collected on different smartphones, and the region to be detected is extracted by the algorithm proposed in this paper. Considering that the fingertip’s pressure will be different during each measurement, this paper makes a comparative analysis of pulse signals extracted under different pressures. In order to verify the accuracy of the algorithm proposed in this paper in heart rate detection, a comparative experiment of heart rate detection was conducted. The results show that the algorithm proposed in this paper can accurately extract human heart rate information and has certain portability, which provides certain theoretical help for further development of physiological monitoring application on smartphone platform.