Clinically, cardiovascular disease (CVD) patients need physicians to suggest different exercise training and rehabilitation procedures to improve their cardiopulmonary function (CPF). In previous studies, several approaches, such as cardiopulmonary exercise testing (CPET), echocardiography and computed tomography angiography (CTA), were proposed to indirectly estimate the rehabilitation effect on CPF. However, the above approached require experienced operators and complex equipment. In this study, a smart and wearable brain oxygenation monitoring system without motion artifact and crosstalk is proposed to estimate the blood circulation state of brain tissue directly during incremental exercise. Moreover, the technique of neural network is also used for classifying different CPF groups from the indexes extracted from the measured hemoglobin parameters. The experimental results show that the defined indexes extracted from the hemoglobin parameters can present the state of CPF, and the proposed smart brain oxygenation monitoring system can also effectively and automatically classify different CPF groups from these indexes via artificial intelligence. The proposed system therefore may assist physicians in the clinical evaluation of the CVD severity and rehabilitation effect on CPF in the future. INDEX TERMS Brain oxygenation, cardiovascular diseases, cardiopulmonary exercise testing, neural network.

This paper examined the plausibility of using 2D images to estimate the center of mass of the human body (CoMb) and the performance using the trajectory of CoMb as an event detector for baseball swing movement. Movement data from three subjects are collected using both a motion capture system and a commercial camera, and the resultant 2D skeleton and CoMb position from the 2D images are obtained using two pose estimation algorithms and an anthropometry model. The errors of the 2D results are 42.67 mm for the skeleton and 50.69 mm for CoMb estimation. 88 baseball swing movement videos are used to illustrate the performance of CoMb trajectory as an event detector with a baseball expert. The agreement was 90.91% between the CoMb trajectory and the baseball expert.

No abstract

No abstract

No abstract