Research of healthy exercise has garnered a keen research for the past few years. It is known that participation in a regular exercise program can help improve various aspects of cardiovascular function and reduce the risk of suffering from illness. But some exercise accidents like dehydration, exertional heatstroke, and even sudden death need to be brought to attention. If these exercise accidents can be analyzed and predicted before they happened, it will be beneficial to alleviate or avoid disease or mortality. To achieve this objective, an exercise health simulation approach is proposed, in which an integrated human thermophysiological model consisting of human thermal regulation model and a nonlinear heart rate regulation model is reported. The human thermoregulatory mechanism as well as the heart rate response mechanism during exercise can be simulated. On the basis of the simulated physiological indicators, a fuzzy finite state machine is constructed to obtain the possible health transition sequence and predict the exercise health status. The experiment results show that our integrated exercise thermophysiological model can numerically simulate the thermal and physiological processes of the human body during exercise and the predicted exercise health transition sequence from finite state machine can be used in healthcare.

Participation in a regular exercise program can improve health status and contribute to an increase in life expectancy. However, exercise accidents like dehydration, exertional heatstroke, syncope, and even sudden death exist. If these accidents can be analyzed or predicted before they happen, it will be beneficial to alleviate or avoid uncomfortable or unacceptable human disease. Therefore, an exercise thermophysiology comfort prediction model is needed. In this paper, coupling the thermal interactions among human body, clothing, and environment (HCE) as well as the human body physiological properties, a human thermophysiology regulatory model is designed to enhance the human thermophysiology simulation in the HCE system. Some important thermal and physiological performances can be simulated. According to the simulation results, a human exercise thermophysiology comfort prediction method based on fuzzy inference system is proposed. The experiment results show that there is the same prediction trend between the experiment result and simulation result about thermophysiology comfort. At last, a mobile application platform for human exercise comfort prediction is designed and implemented.

This study deals with an important issue that is often encountered with the registration of remote sensing images which are obtained at different times and/or through inter/intra sensors. Remote sensing images may differ significantly in graylevel characteristics and contrast, among other aspects. Thus, it may be difficult to apply directly area-based approaches which are dependent on image intensity values. In this work, a novel approach for automatic image registration based on Gaussian-Hermite moments and the Pseudo-RANSAC algorithm is proposed. The problem of intensity difference commonly incurred in multi-temporal or multimodal remote sensing image registration is tackled using features that are invariant to intensity mapping during the feature point matching process. In particular, the feature points are herein represented by a range of newly introduced Gaussian-Hermite moments, and the corresponding feature points in a certain reference image are sought with the Euclidean distance measure. Moreover, an improved RANdom SAmple Consensus (RANSAC) algorithm is presented, reducing computational time complexity while improving performance in stability and accuracy. The final warping of images according to their refined feature points is conducted with bilinear interpolation. The proposed approach has been successfully applied to register synthetic and real remote sensing images, demonstrating its efficacy with systematic experimental evaluations.

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