The examination and management of mental stress has been recognized as a vital examination scheme. However, present methods for stress-assessment rely mainly on questionnaire surveys, which can provide the changing trend of a characteristic value under stress state, but fail to determine the specific stress level. To tackle this problem, this study developed a non-invasive and objective stress measurement system signals collected by biosensors. A wearable sensor was used to collect PPG signal in real-time, and the short-term heart rate variability was measured, and SDNN and LF/HF were extracted as characteristic values in the time and frequency-domains to detect mental stress. In order to examine the performance of the proposed system, physiological signals of 20 volunteers under stress and normal states were collected. The stress state was identified according to the stress-state table of each characteristic value. The proposed system resulted in a stress-prediction accuracy of 95% in distinguishing between low- and high-stress levels. The results indicated that SDNN and LF/HF are effective indicators for the detection of mental stress, and can effectively improve the accuracy of stress-recognition.
In order to objectively and quantitatively evaluate the facial morphology of patients in plastic surgery, the key points of facial aesthetics are extracted by interaction and specified face database, and the geometric features are calculated to obtain accurate quantitative face data, and the aesthetic evaluation model is established. Face assessment, eyebrows and eyes overall assessment and nose assessment were carried out for the subjects. The results showed that the model could objectively give the facial score of the subjects, find out the deviation from the standard, and provide objective and effective guidance for further plastic surgery.
ABO blood type automatic recognition can realize the automatic detection of blood type, effectively improve the speed and accuracy of blood type analysis, and has a wide range of clinical applications. In this paper, an automatic blood type recognition algorithm based on image processing is designed. Firstly, the image of blood type card is preprocessed, including image enhancement and median filtering. Then, the micro column tubules are segmented by template matching. Then, the threshold analysis is carried out, and the gray image is transformed into a binary image. Finally, the distribution of red blood cell aggregates in micro column tubules is identified, and determine the ABO blood type of the sample. The experimental results show that the algorithm can effectively segment the micro column tubules on the blood type card, realize the effective recognition of red blood cell distribution, and complete the effective interpretation of blood group.
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