Integrated UWB/MIMU Sensor System for Position Estimation towards an Accurate Analysis of Human Movement: A Technical Review.

Integrated Ultra-wideband (UWB) and Magnetic Inertial Measurement Unit (MIMU) sensor systems have been gaining popularity for pedestrian tracking and indoor localization applications, mainly due to their complementary error characteristics that can be exploited to achieve higher accuracies via a data fusion approach. These integrated sensor systems have the potential for improving the ambulatory 3D analysis of human movement (estimating 3D kinematics of body segments and joints) over systems using only on-body MIMUs. For this, high accuracy is required in the estimation of the relative positions of all on-body integrated UWB/MIMU sensor modules. So far, these integrated UWB/MIMU sensors have not been reported to have been applied for full-body ambulatory 3D analysis of human movement. Also, no review articles have been found that have analyzed and summarized the methods integrating UWB and MIMU sensors for on-body applications. Therefore, a comprehensive analysis of this technology is essential to identify its potential for application in 3D analysis of human movement. This article thus aims to provide such a comprehensive analysis through a structured technical review of the methods integrating UWB and MIMU sensors for accurate position estimation in the context of the application for 3D analysis of human movement. The methods used for integration are all summarized along with the accuracies that are reported in the reviewed articles. In addition, the gaps that are required to be addressed for making this system applicable for the 3D analysis of human movement are discussed.

Virus detection using nanoparticles and deep neural network-enabled smartphone system.

Emerging and reemerging infections present an ever-increasing challenge to global health. Here, we report a nanoparticle-enabled smartphone (NES) system for rapid and sensitive virus detection. The virus is captured on a microchip and labeled with specifically designed platinum nanoprobes to induce gas bubble formation in the presence of hydrogen peroxide. The formed bubbles are controlled to make distinct visual patterns, allowing simple and sensitive virus detection using a convolutional neural network (CNN)-enabled smartphone system and without using any optical hardware smartphone attachment. We evaluated the developed CNN-NES for testing viruses such as hepatitis B virus (HBV), HCV, and Zika virus (ZIKV). The CNN-NES was tested with 134 ZIKV- and HBV-spiked and ZIKV- and HCV-infected patient plasma/serum samples. The sensitivity of the system in qualitatively detecting viral-infected samples with a clinically relevant virus concentration threshold of 250 copies/ml was 98.97% with a confidence interval of 94.39 to 99.97%.

Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology.

Embryo assessment and selection is a critical step in an in vitro fertilization (IVF) procedure. Current embryo assessment approaches such as manual microscopy analysis done by embryologists or semi-automated time-lapse imaging systems are highly subjective, time-consuming, or expensive. Availability of cost-effective and easy-to-use hardware and software for embryo image data acquisition and analysis can significantly empower embryologists towards more efficient clinical decisions both in resource-limited and resource-rich settings. Here, we report the development of two inexpensive (<$100 and <$5) and automated imaging platforms that utilize advances in artificial intelligence (AI) for rapid, reliable, and accurate evaluations of embryo morphological qualities. Using a layered learning approach, we have shown that network models pre-trained with high quality embryo image data can be re-trained using data recorded on such low-cost, portable optical systems for embryo assessment and classification when relatively low-resolution image data are used. Using two test sets of 272 and 319 embryo images recorded on the reported stand-alone and smartphone optical systems, we were able to classify embryos based on their cell morphology with >90% accuracy.

Automated smartphone-based system for measuring sperm viability, DNA fragmentation, and hyaluronic binding assay score.

The fundamental test for male infertility, semen analysis, is mostly a manually performed subjective and time-consuming process and the use of automated systems has been cost prohibitive. We have previously developed an inexpensive smartphone-based system for at-home male infertility screening through automatic and rapid measurement of sperm concentration and motility. Here, we assessed the feasibility of using a similar smartphone-based system for laboratory use in measuring: a) Hyaluronan Binding Assay (HBA) score, a quantitative score describing the sperm maturity and fertilization potential in a semen sample, b) sperm viability, which assesses sperm membrane integrity, and c) sperm DNA fragmentation that assesses the degree of DNA damage. There was good correlation between the manual analysis and smartphone-based analysis for the HBA score when the device was tested with 31 fresh, unprocessed human semen samples. The smartphone-based approach performed with an accuracy of 87% in sperm classification when the HBA score was set at manufacturer's threshold of 80. Similarly, the sperm viability and DNA fragmentation tests were also shown to be compatible with the smartphone-based system when tested with 102 and 47 human semen samples, respectively.

Rapid, label-free CD4 testing using a smartphone compatible device.

The most recent guidelines have called for a significant shift towards viral load testing for HIV/AIDS management in developing countries; however point-of-care (POC) CD4 testing still remains an important component of disease staging in multiple developing countries. Advancements in micro/nanotechnologies and consumer electronics have paved the way for mobile healthcare technologies and the development of POC smartphone-based diagnostic assays for disease detection and treatment monitoring. Here, we report a simple, rapid (30 minutes) smartphone-based microfluidic chip for automated CD4 testing using a small volume (30 µL) of whole blood. The smartphone-based device includes an inexpensive (<$5) cell phone accessory and a functionalized disposable microfluidic device. We evaluated the performance of the device using spiked PBS samples and HIV-infected and uninfected whole blood, and compared the microfluidic chip results with the manual analysis and flow cytometry results. Through t-tests, Bland-Altman analyses, and regression tests, we have shown a good agreement between the smartphone-based test and the manual and FACS analysis for CD4 count. The presented technology could have a significant impact on HIV management in developing countries through providing a reliable and inexpensive POC CD4 testing.