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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.

Exploring shape variations on virtual garments is significant but challenging to the aspect of 3D garment modeling. In this paper, we propose a data-driven editing framework for automatic 3D garment modeling, which includes semantic garment segmentation, probabilistic reasoning for component suggestion, and garment component merging. The key idea in this work is to develop a simple but effective garment synthesis that utilizes a continuous style description, which can be characterized by the ratio of area and boundary length on garment components. First, a semi-supervised learning algorithm is proposed to simultaneously segment and label the components in 3D garments. Second, a set of matchable probability measurement is applied to recommend components that can be regarded as a new 3D garment. Third, a variation synthesis is developed to satisfy the garment style criteria while ensuring the realistic-looking plausibility of the results. As demonstrated by the experiments, our method is able to generate various reasonable garments with material effects to enrich existing 3D garments.

Motion classification system based on surface Electromyography (sEMG) pattern recognition has achieved good results in experimental condition. But it is still a challenge for clinical implement and practical application. Many factors contribute to the difficulty of clinical use of the EMG based dexterous control. The most obvious and important is the noise in the EMG signal caused by electrode shift, muscle fatigue, motion artifact, inherent instability of signal and biological signals such as Electrocardiogram. In this paper, a novel method based on Canonical Correlation Analysis (CCA) was developed to eliminate the reduction of classification accuracy caused by electrode shift. The average classification accuracy of our method were above 95% for the healthy subjects. In the process, we validated the influence of electrode shift on motion classification accuracy and discovered the strong correlation with correlation coefficient of >0.9 between shift position data and normal position data.