Abstract:In this paper, a pointing gesture recognition method is proposed for human-robot interaction. The pointing direction of the human partner is obtained by extracting the joint coordinates and computing through vector calculations. 3D to 2D mapping is implemented to build a top-view 2D map with respect to the actual ground circumstance. Using this method, robot is able to interpret the human partner's 3D pointing gesture based on the coordinate information of his/her shoulder and hand. Besides this, speed control of robot can be achieved by adjusting the position of the human partner's hand relative to the head. The recognition performance and viability of the system are tested through quantitative experiments.

Background Although several meta‐analyses have examined the effects of off‐label underdosing of nonvitamin K antagonist oral anticoagulants (NOACs) compared with their recommended doses in patients with atrial fibrillation (AF), they combined different kinds of NOACs in their primary analyses. Herein, we first conducted a meta‐analysis to separately assess the effects of off‐label underdosing versus on‐label dosing of four individual NOACs on adverse outcomes in the AF population. Methods The PubMed and Embase database were systemically searched until November 2021 to identify the relevant studies. Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were pooled by utilizing a random‐effects model. Results A total of nine studies with 144,797 patients taking NOACs were included in the meta‐analysis. In the pooled analysis, off‐label underdosing of rivaroxaban was related to an increased risk of stroke or systemic embolism (HR = 1.31, 95% CI 1.05–1.63; p = .02), whereas off‐label underdosing of apixaban was associated with a higher risk of all‐cause death (HR = 1.21, 95% CI 1.05–1.40; p = .01). When comparing off‐label underdosing versus on‐label dosing of dabigatran or edoxaban, no differences were found in the primary and secondary clinical outcomes. Conclusion Off‐label underdosing of rivaroxaban may increase the risk of stroke or systematic embolism, whereas off‐label underdosing of apixaban may heighten the incidence of all‐cause death.

MRI and CT are both important medical imaging modalities, but MRI and CT imaging are done in different ways, each with its own advantages and disadvantages. Obtaining both images at the same time can help physicians make better decisions about treatment options. However, due to various limitations, some patients can only obtain one type of image. Therefore, it is necessary to find a well-performing GAN to transform MRI and CT images. In this paper, the effect of Cycle-GAN with different activation functions is compared, such as LeakyRELU, and different number of layers in MRI-CT conversion. Also, this article compares the effects of Cycle-GAN and UNet-GAN. The results indicate that the Cycle-GAN model using LeakyRELU as the activation function is better than the Cycle-GAN model using RELU as the activation function. Second, the effect of deepening the layers of the GAN model is worse than that of the base model. And the effect of UNet-GAN is similar to that of Cycle-GAN. This is not quite as expected, because Cycle-GAN has one more discriminator than UNet-GAN, and the effect should be better. But the experimental results do not confirm this conclusion.

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