Video-assisted smart health monitoring for affliction determination based on fog analytics.

Satisfying the expectations of quality living is essential for smart healthcare. Therefore, the determination of health afflictions in real-time has been considered as one of the most necessary parts of medical or assistive-care domain. In this article, a novel fog analytic-assisted deep learning-enabled physical stance-based irregularity recognition framework is presented to enhance personal living satisfaction of an individual. To increase the utility of the proposed framework for assistive-care, an attempt has been made to record predicted activity scores on cloud by following the continuous time series policy to provide future health references to authorized medical specialist. Furthermore, a smart two-phased decision generation mechanism is proposed to intimate medical specialist and caretakers about the current physical status of an individual in real-time. The generation of the alert is directly proportional to the predicted physical irregularity and the scale of health severity. The experimental results highlight the advantages of fog analytics that helps to increase the recognition rate up to 46.45% for 40 FPS and 45.72% for 30 FPS against cloud-based monitoring solutions. The calculated outcomes justify the superiority of the proposed fog analytics monitoring solution over the conventional cloud-based monitoring solutions by achieving high activity prediction accuracy and less latency rate in decision making.

Development of cost effective metal oxide semiconductor based gas sensor over flexible chitosan/PVP blended polymeric substrate.

In the present work, biodegradable and flexible chitosan/polyvinylpyrrolidone (CHP) polymeric substrate was fabricated by solvent casting method. This is a novel demonstration of the combination of natural polymer (chitosan) and synthetic polymer (PVP) for next-generation semiconductor device applications. The ZnO thin films were successfully synthesized on these polymeric substrates by facile drop-casting method for gas sensing applications. The hydrogen gas sensing properties of ZnO deposited on the polymeric substrate and SiO2 substrate show similar performance. The structural, morphological, optical, thermal, and tensile strength of the CHP substrate were studied using X-ray diffraction (XRD), Scanning electron microscopy (SEM), UV-visible spectroscopy, Derivative thermogravimetric analysis (DTG), and Universal testing machine (UTM), respectively. Our study suggests that the biodegradable CH/PVP flexible polymeric substrate provides a new way for the implementation of an eco-friendly green substrate in numerous electronic device applications.

District level inequality in reproductive, maternal, neonatal and child health coverage in India.

BACKGROUND: As India already missed maternal and child health related millennium development goals, the maternal and child health outcomes are a matter of concern to achieve sustainable development goals (SDGs). This study is focused to assess the gap in coverage and inequality of various reproductive, maternal, neonatal and child health (RMNCH) indicators in 640 districts of India, using data from most recent round of National Family Health Survey. METHODS: A composite index named Coverage Gap Index (CGI) was calculated, as the weighted average of eight preventive maternal and child care interventions at different administrative levels. Bivariate and spatial analysis were used to understand the geographical diversity and spatial clustering in districts of India. A socio-economic development index (SDI) was also derived and used to assess the interlinkages between CGI and development. The ratio method was used to assess the socio-economic inequality in CGI and its component at the national level. RESULTS: The average national CGI was 26.23% with the lowest in Kerala (10.48%) and highest in Nagaland (55.07%). Almost half of the Indian districts had CGI above the national average and mainly concentrated in high focus states and north-eastern part. From the geospatial analysis of CGI, 122 districts formed hotspots and 164 districts were in cold spot. The poorest households had 2.5 times higher CGI in comparison to the richest households and rural households have 1.5 times higher CGI as compared to urban households. CONCLUSION: Evidence from the study suggests that many districts in India are lagging in terms of CGI and prioritize to achieve the desired level of maternal and child health outcomes. Efforts are needed to reduce the CGI among the poorest and rural resident which may curtail the inequality.