Background With the rise of mobile medicine, the development of new technologies such as smart sensing, and the popularization of personalized health concepts, the field of smart wearable devices has developed rapidly in recent years. Among them, medical wearable devices have become one of the most promising fields. These intelligent devices not only assist people in pursuing a healthier lifestyle but also provide a constant stream of health care data for disease diagnosis and treatment by actively recording physiological parameters and tracking metabolic status. Therefore, wearable medical devices have the potential to become a mainstay of the future mobile medical market. Objective Although previous reviews have discussed consumer trends in wearable electronics and the application of wearable technology in recreational and sporting activities, data on broad clinical usefulness are lacking. We aimed to review the current application of wearable devices in health care while highlighting shortcomings for further research. In addition to daily health and safety monitoring, the focus of our work was mainly on the use of wearable devices in clinical practice. Methods We conducted a narrative review of the use of wearable devices in health care settings by searching papers in PubMed, EMBASE, Scopus, and the Cochrane Library published since October 2015. Potentially relevant papers were then compared to determine their relevance and reviewed independently for inclusion. Results A total of 82 relevant papers drawn from 960 papers on the subject of wearable devices in health care settings were qualitatively analyzed, and the information was synthesized. Our review shows that the wearable medical devices developed so far have been designed for use on all parts of the human body, including the head, limbs, and torso. These devices can be classified into 4 application areas: (1) health and safety monitoring, (2) chronic disease management, (3) disease diagnosis and treatment, and (4) rehabilitation. However, the wearable medical device industry currently faces several important limitations that prevent further use of wearable technology in medical practice, such as difficulties in achieving user-friendly solutions, security and privacy concerns, the lack of industry standards, and various technical bottlenecks. Conclusions We predict that with the development of science and technology and the popularization of personalized health concepts, wearable devices will play a greater role in the field of health care and become better integrated into people’s daily lives. However, more research is needed to explore further applications of wearable devices in the medical field. We hope that this review can provide a useful reference for the development of wearable medical devices.

No abstract

Experimental measurements in laboratory-scale turbulent burners with well-controlled boundary and flow configurations can provide valuable data for validating models of turbulence-chemistry interactions applicable to the design and analysis of practical combustors. This paper reports on the design of two canonical nonpremixed turbulent jet burners for use with undiluted gaseous and liquid hydrocarbon fuels, respectively. Previous burners of this type have only been developed for fuels composed of H(2), CO, and/or methane, often with substantial dilution. While both new burners are composed of concentric tubes with annular pilot flames, the liquid-fuel burner has an additional fuel vaporization step and an electrically heated fuel vapor delivery system. The performance of these burners is demonstrated by interrogating four ethylene flames and one flame fueled by a simple JP-8 surrogate. Through visual observation, it is found that the visible flame lengths show good agreement with standard empirical correlations. Rayleigh line imaging demonstrates that the pilot flame provides a spatially homogeneous flow of hot products along the edge of the fuel jet. Planar imaging of OH laser-induced fluorescence reveals a lack of local flame extinction in the high-strain near-burner region for fuel jet Reynolds numbers (Re) less than 20,000, and increasingly common extinction events for higher jet velocities. Planar imaging of soot laser-induced incandescence shows that the soot layers in these flames are relatively thin and are entrained into vortical flow structures in fuel-rich regions inside of the flame sheet.

Inspired by the slight acidic microenvironment, a variety of pH-responsive nanomaterials are designed for high antibacterial therapy through improving the ability of drug penetration and retention to enhance the therapeutic...

No abstract

Photoacoustic tomography is a technique to reconstruct the image of light energy absorption distribution in tissues based on the detected photoacoustic signals. In recent years, this research field has been greatly developed, and its application range is wide, including anatomy, functionality, and molecular imaging. However, the conversion efficiency of photoacoustic effect from light to sound is quite low, which leads to the low signal-to-noise ratio of photoacoustic signal and the poor quality of reconstructed photoacoustic image. The traditional method to improve the signal-to-noise ratio of photoacoustic signals is data averaging method, but it seriously limits the imaging speed due to multiple acquisition. Without sacrificing signal fidelity and imaging speed, an empirical mode decomposition (EMD) combined with conditional mutual information de-noising algorithm for photoacoustic tomography is proposed in this paper. The simulation results and experimental results of photoacoustic signal de-noising achieve significant improvement of signal-to-noise ratio of photoacoustic signal and the enhancement of contrast of the reconstructed image. The simulation results and experimental results show that EMD combined with mutual information method improves at least 2 dB and 3 dB, respectively, more than traditional wavelet threshold method and band-pass filter. The improvement of contrast-to-noise ratio is more than 2 dB and 3 dB, respectively, more than traditional wavelet threshold method and band-pass filter.

Nowadays, breast cancer has increasingly threatened the health of human, especially females. However, breast cancer is still hard to detect in the early stage, and the diagnostic procedure can be timeconsuming with abundant expertise needed. In this paper, we explored the deep learning algorithms in emerging photoacoustic tomography for breast cancer diagnostics. Specifically, we used a pre-processing algorithm to enhance the quality and uniformity of input breast cancer images and a transfer learning method to achieve better classification performance. Besides, by comparing the area under the curve, sensitivity, and specificity of support vector machine with AlexNet and GoogLeNet, it can be concluded that the combination of deep learning and photoacoustic imaging has the potential to achieve important impact on clinical diagnostics. Finally, according to the breast imaging reporting and data-system levels, we divided breast cancer images into six grades and designed a segmentation software for identifying the six grades of breast cancer. Then, we tested based on MAMMOGRAPHYC IMAGES DATABASE FROM LAPIMO EESC/USP (Laboratory of Analysis and Processing of Medical and Dental Images) to verify the accuracy of our segmentation method, which showed a satisfactory result. INDEX TERMS Photoacoustic imaging, deep learning, breast cancer diagnosis, image classification and segmentation.

No abstract