Visual Gait Analysis Based on UE4.

With the development of artificial intelligence technology, virtual reality technology has been widely used in the medical and entertainment fields, as well as other fields. This study is supported by the 3D modeling platform in UE4 platform technology and designs a 3D pose model based on inertial sensors through blueprint language and C++ programming. It can vividly display changes in gait, as well as changes in angles and displacements of 12 parts such as the big and small legs and arms. It can be used to combine with the module of capturing motion which is based on inertial sensors to display the 3D posture of the human body in real-time and analyze the motion data. Each part of the model contains an independent coordinate system, which can analyze the angle and displacement changes of any part of the model. All joints of the model are interrelated, the motion data can be automatically calibrated and corrected, and errors measured by an inertial sensor can be compensated, so that each joint of the model will not separate from the whole model and there will not occur actions that against the human body's structures, improving the accuracy of the data. The 3D pose model designed in this study can correct motion data in real time and display the human body's motion posture, which has great application prospects in the field of gait analysis.

Online Monitoring of Seawater Carbon Dioxide Based on an Infrared Rear Beam Splitter.

The ocean is one of the most extensive ecosystems on Earth and can absorb large amounts of carbon dioxide. Changes in seawater carbon dioxide concentrations are one of the most important factors affecting marine ecosystems. Excess carbon dioxide can lead to ocean acidification, threatening the stability of marine ecosystems and species diversity. Dissolved carbon dioxide detection in seawater has great scientific significance. Conducting online monitoring of seawater carbon dioxide can help to understand the health status of marine ecosystems and to protect marine ecosystems. Current seawater detection equipment is large and costly. This study designed a low-cost infrared carbon dioxide detection system based on molecular theory. Using the HITRAN database, the absorption spectra and coefficients of carbon dioxide molecules under different conditions were calculated and derived, and a wavelength of 2361 cm-1 was selected as the measurement channel for carbon dioxide. In addition, considering the interference effect of direct light, an infrared post-splitting method was proposed to eliminate the interference of light and improve the detection accuracy of the system. The system was designed for the online monitoring of carbon dioxide in seawater, including a peristaltic pump to accelerate gas-liquid separation, an optical path structure, and carbon dioxide concentration inversion. The experimental results showed that the standard deviation of the gas test is 3.05, the standard deviation of the seawater test is 6.04, and the error range is within 20 ppm. The system can be flexibly deployed and has good stability and portability, which can meet the needs of the online monitoring of seawater carbon dioxide concentration.

Paroxysmal atrial fibrillation recognition based on multi-scale Rényi entropy of ECG.

BACKGROUND: Atrial fibrillation (AF) is a common type of arrhythmia disease, which has a high morbidity and can lead to some serious complications. The ability to detect and in turn prevent AF is extremely significant to the patient and clinician. OBJECTIVE: Using ECG to detect AF and develop a robust and effective algorithm is the primary objective of this study. METHODS: Some studies show that after AF occurs, the regulatory mechanism of vagus nerve and sympathetic nerve will change. Each R-R interval will be absolutely unequal. After studying the physiological mechanism of AF, we will calculate the Rényi entropy of the wavelet coefficients of heart rate variability (HRV) in order to measure the complexity of PAF signals, as well as extract the multi-scale features of paroxysmal atrial fibrillation (PAF). RESULTS: The data used in this study is obtained from MIT-BIH PAF Prediction Challenge Database and the correct rate in classifying PAF patients from normal persons is 92.48%. CONCLUSIONS: The results of this experiment proved that AF could be detected by using this method and, in turn, provide opinions for clinical diagnosis.