With the development of the Internet of Things technology and the continuous improvement of manufacturing processes, smart devices continue to evolve. In the existing research on human motion posture detection, there are relatively few portable wearable devices. Therefore, research on human motion posture detection based on smart wearable devices will be an important direction for future development. In this paper, a smart phone with a built-in triaxial acceleration sensor is used as a data acquisition facility to simulate the wearable device's experiment on the daily movement of the human body. The three-dimensional acceleration data during the motion is collected, and the related methods are proposed to detect the human body motion posture, and further analyze the transition between various postures, and detect the special fall posture. Starting from the two aspects of time domain and frequency domain, the analysis algorithm of human motion attitude detection is expounded. Using the time domain algorithm to detect the current motion posture of the human body, the current motion posture of the human body can be quickly detected. However, if we simply use time-domain-based detection methods, it is easy to cause errors in attitude detection. In order to improve this detection error, we combine the frequency domain analysis method, use the acceleration modulus algorithm and perform FFT (Fast Fourier Transformation) to analyze the frequency domain characteristics of the signal to distinguish the human body's motion posture and improve the basic daily posture detection of the human body. The results show that the proposed method can eliminate large noise interference and reduce the missed rate and false positive rate of human attitude detection. INDEX TERMS Motion attitude detection, wearable sensor network, acceleration modulus algorithm, motion attitude feature analysis, attitude measurement.

This paper explores the effects of wearable medical devices (WMDs) in vital sign monitoring, which is the key to telemedicine rehabilitation. First, 60 young patients were selected from the department of oral and maxillofacial surgery in a Chinese hospital. The vital signs of the patients, namely, ear temperature, pulse, blood oxygen saturation, and blood pressure, were measured by both the WMDs and the traditional devices. The measurements were carried out at 6:00, 10:00, 14:00, and 18:00 on the first day of each month in 2018. The comparison between the measured results shows that the WMDs and the traditional devices have no difference in the measured data and measurement time of any vital sign (P > 0.05), but the WMDs consumed much less time in data transcription and the entire measurement process. This means the WMDs are suitable for vital sign measurement in telemedicine rehabilitation. INDEX TERMS Wearable medical devices (WMDs), vital sign measurement, Bluetooth, Internet of Things (IoT).

The attack of pseudo base station is one of the security problems on mobile terminals, which affects normal communications and disguises as legit users for illegal purposes. To prevent pseudo base stations from interfering with users, this paper proposes a joint detection method that can judge pseudo base station. The abnormal access parameters in the process of terminal access to pseudo base station are divided into two categories, one is base station parameters, and the other is terminal parameters. Based on these feature parameters including LAC, CI, RSSI and mobile phone mode, the method concludes algorithm by Naive Bayesian classification. The experiment result shows that the recall rate increases to 70% and the false positive rate decreases to 30%, which means this method is better than traditional ways.

<p>Deblurring of motion images is a part of the field of image restoration. The deblurring of motion images is not only difficult to estimate the motion parameters, but also contains complex factors such as noise, which makes the deblurring algorithm more difficult. Image deblurring can be divided into two categories: one is the non-blind image deblurring with known fuzzy kernel, and the other is the blind image deblurring with unknown fuzzy kernel. The traditional motion image deblurring networks ignore the non-uniformity of motion blurred images and cannot effectively recover the high frequency details and remove artifacts. In this paper, we propose a new generative adversarial network based on multi-feature fusion strategy for motion image deblurring. An adaptive residual module composed of deformation convolution module and channel attention module is constructed in the generative network. Where, the deformation convolution module learns the shape variables of motion blurred image features, and can dynamically adjust the shape and size of the convolution kernel according to the deformation information of the image, thus improving the ability of the network to adapt to image deformation. The channel attention module adjusts the extracted deformation features to obtain more high-frequency features and enhance the texture details of the restored image. Experimental results on public available GOPRO dataset show that the proposed algorithm improves the peak signal-to-noise ratio (PSNR) and is able to reconstruct high quality images with rich texture details compared to other motion image deblurring methods.</p> <p>&nbsp;</p>

The application of artificial intelligence has realized the transformation of people's production and lifestyle, and also promoted the progress of physical education and comprehensive health quality. The application of artificial intelligence in the current physical education movement is increasing. By utilizing its advanced method of virtual simulation technology, the purpose of this paper is to realize the interventional research on the physical education movement and comprehensive health quality in the environment of artificial intelligence. This paper proposes to use the virtual simulation technology and Kinect algorithm in artificial intelligence to design the virtual sports simulation teaching mode. The functional module design part where the Kinect algorithm helps the teaching of virtual sports simulation experiments, which is helpful to analyze and solve the objective system imbalance and ecological imbalance in online physical education teaching. By using the principles and rules of the Mean Shift image segmentation algorithm for reference, the investigation and research on the comprehensive health quality of students are carried out, so as to realize the ecologicalization of the virtual sports school. In the investigation and research on the comprehensive quality of students, the results show that the overall quality of these students who has reached the level of qualified or unqualified is accounting for about 30% of the total number. It is worth noting that in terms of scientific and cultural quality, only 43.34% of all students have excellent grades. It can be seen that the important training goal of current school research is how to use reasonable and effective methods and strategies to improve students' scientific and cultural level, and improve students' other comprehensive scores at the same time.