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The accurate estimation of a 3D human pose is of great importance in many fields, such as human–computer interaction, motion recognition and automatic driving. In view of the difficulty of obtaining 3D ground truth labels for a dataset of 3D pose estimation techniques, we take 2D images as the research object in this paper, and propose a self-supervised 3D pose estimation model called Pose ResNet. ResNet50 is used as the basic network for extract features. First, a convolutional block attention module (CBAM) was introduced to refine selection of significant pixels. Then, a waterfall atrous spatial pooling (WASP) module is used to capture multi-scale contextual information from the extracted features to increase the receptive field. Finally, the features are input into a deconvolution network to acquire the volume heat map, which is later processed by a soft argmax function to obtain the coordinates of the joints. In addition to the two learning strategies of transfer learning and synthetic occlusion, a self-supervised training method is also used in this model, in which the 3D labels are constructed by the epipolar geometry transformation to supervise the training of the network. Without the need for 3D ground truths for the dataset, accurate estimation of the 3D human pose can be realized from a single 2D image. The results show that the mean per joint position error (MPJPE) is 74.6 mm without the need for 3D ground truth labels. Compared with other approaches, the proposed method achieves better results. [ABSTRACT FROM AUTHOR]

Background: An increasing number of systematic reviews (SRs) in the environmental field have been published in recent years as a result of the global concern about the health impacts of air pollution and temperature. However, no study has assessed and compared the methodological and reporting quality of SRs on the health effects of air pollutants and extreme temperatures. This study aims to assess and compare the methodological and reporting quality of SRs on the health effects of ambient air pollutants and extreme temperatures. Methods: PubMed, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, Web of Science, and Epistemonikos databases were searched. Two researchers screened the literature and extracted information independently. The methodological quality of the SRs was assessed through A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2). The reporting quality was assessed through Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA). Results: We identified 405 SRs (286 for air pollution, 108 for temperature, and 11 for the synergistic effects). The methodological and reporting quality of the included SRs were suboptimal, with major deficiencies in protocol registration. The methodological quality of SRs of air pollutants was better than that of temperature, especially in terms of satisfactory explanations for any heterogeneity (69.6% v. 45.4%). The reporting quality of SRs of air pollution was better than temperature, however, adherence to the reporting of the assessment results of risk of bias in all SRs (53.5% v. 34.3%) was inadequate. Conclusions: Methodological and reporting quality of SRs on the health effect of air pollutants were higher than those of temperatures. However, deficiencies in protocol registration and the assessment of risk of bias remain an issue for both pollutants and temperatures. In addition, developing a risk-of-bias assessment tool applicable to the temperature field may improve the quality of SRs. [ABSTRACT FROM AUTHOR]

To address the dangerous driving behaviors prevalent among current car drivers, it is necessary to provide real-time, accurate warning and correction of driver's driving behaviors in a small, movable, and enclosed space. In this paper, we propose a method for detecting dangerous behaviors based on frequency-modulated continuous-wave radar (mm-DSF). The highly packaged millimeter-wave radar chip has good in-vehicle emotion recognition capability. The acquired millimeter-wave differential frequency signal is Fourier-transformed to obtain the intermediate frequency signal. The physiological decomposition of the local micro-Doppler feature spectrum of the target action is then used as the eigenvalue. Matrix signal intensity and clutter filtering are performed by analyzing the signal echo model of the input channel. The signal classification is based on the estimation and variety of the feature vectors of the target key actions using a modified and optimized level fusion method of the SlowFast dual-channel network. Nine typical risky driving behaviors were set up by the Dula Hazard Questionnaire and TEIQue-SF, and the accuracy of the classification results of the self-built dataset was analyzed to verify the high robustness of the method. The recognition accuracy of this method increased by 1.97% compared with the traditional method. [ABSTRACT FROM AUTHOR]

The positioning of indoor electronic devices is an essential part of human–computer interaction, and the accuracy of positioning affects the level of user experience. Most existing methods for RF-based device localization choose to ignore or remove the impact of multipath effects. However, exploiting the multipath effect caused by the complex indoor environment helps to improve the model's localization accuracy. In response to this question, this paper proposes a multipath-assisted localization (MAL) model based on millimeter-wave radar to achieve the localization of indoor electronic devices. The model fully considers the help of the multipath effect when describing the characteristics of the reflected signal and precisely locates the target position by using the MAL area formed by the reflected signal. At the same time, for the situation where the radar in the traditional Single-Input Single-Output (SISO) mode cannot obtain the 3D spatial position information of the target, the advantage of the MAL model is that the 3D information of the target can be obtained after the mining process of the multipath effect. Furthermore, based on the original hardware, it can achieve a breakthrough in angular resolution. Experiments show that our proposed MAL model enables the millimeter-wave multipath positioning model to achieve a 3D positioning error within 15 cm. [ABSTRACT FROM AUTHOR]

In pharmaceutical warehousing operations, a scientific and reasonable medicine and shelf matching strategy can improve medicine shelving efficiency and manual work efficiency. However, the traditional matching strategy has the problems of static matching and low matching efficiency, so this paper proposes the matching algorithm for drugs and shelves based on the fuzzy analytic hierarchy process and Bayesian convolutional neural network (FAHP-BCNN). First, we propose a drug-shelf matching degree model and discuss the influence of the dynamic matching process on the matching results by studying two parts: the attribute matching degree and the dynamic matching influence degree. Secondly, this paper quantitatively assessed the importance of the two matches by means of the fuzzy analytic hierarchy process. Finally, we mapped the massive matching information onto Bayesian convolutional neural network nodes, constructed a dynamic matching network model, and obtained competitive matching strategies and results. The experimental results show that the FAHP-BCNN algorithm exhibits better matching results in all three scenarios with different quantity ratios of drugs and shelf matching. Compared with the fixed cargo matching strategy of the ABC classification method, the FAHP-BCNN algorithm shows excellent performance in both the manual walking distance index and fatigue index. In summary, the drug-shelf matching algorithm based on FAHP-BCNN is effective and can provide theoretical support for pharmaceutical warehousing enterprises to perform drug-shelf matching. [ABSTRACT FROM AUTHOR]