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Background: Dengue is a mosquito-borne viral disease whose severity varies from a mild undifferentiated fever to severe fatal form. The objectives of this study were to determine the prevalence and risk factors of severe dengue. Subjects and Methods: This nested case–control study was done in a medical college hospital in South India from October 2019 to January 2020. Children between 1 month and 12 years of age who presented with fever without focus and tested positive for dengue serology were included. Demographic data, anthropometry, clinical features, and laboratory parameters were noted down from all children. Severity of illness was classified according to the World Health Organization dengue guidelines 2009. Prevalence of severe dengue was expressed in proportion with 95% confidence interval (CI). Bivariate analysis was performed using Chi-square test, Fisher's test, and Student's t-test, and a P < 0.05 was considered statistically significant. Results: Out of 370 children, 192 (51.9%) had dengue without warning signs, 168 (45.4%) had dengue with warning signs, and 10 had severe dengue. The prevalence (95% CI) of severe dengue was 2.7% (1.3%–4.9%). Abdominal pain/tenderness (odd's ratio [OR] - 7.429), facial puffiness (OR - 4.333), hepatomegaly (OR - 3.857), and pleural effusion (OR - 13.222) were the significant risk factors for severe dengue. Conclusions: The significant risk factors for severe dengue were abdominal pain/tenderness, facial puffiness, hepatomegaly, and pleural effusion.

Gesture controlled wheelchair is designed and intended to perform specific job functions combining software interfacing with the hardware along with the embedded electronics, through hand controlled gestures. In this paper, a novel multiple control option-based wheelchair is proposed to cater to the needs of handicapped patients and senior citizens. This work is aimed at designing, integrating, coding, interfacing and testing a fully motorized, gesture controlled wheelchair which involves the physical implementation of the hardware, the software interfacing and testing. This Smart wheelchair is furnished with sensors, interactive cameras controlled by a processor unit that can control the various tasks it is designed to perform. The proposed Smart Wheelchair utilizes micro-electro-mechanical-system (MEMS) accelerometers to recognize various hand gestures performing a specific task. It is an accelerometer-based system which controls a robotic arm wirelessly using a small, low-cost, 3-axis degrees of freedom accelerometer through RF signals. The gestures are also trained by machine learning to identify the training patterns by choosing the appropriate mode selection. The hand gestures are thus based on motion sensor and pattern recognition. Apart from these hand gestures, the smart wheelchair can also be controlled by an android mobile app through a smartphone or by a hand held device that can navigate the smart wheelchair in the forward, backward, right, left directions and also perform a stop operation. The Smart Wheelchair also detects an obstruction present on the way of navigation and is operated in accordance with the command. An interactive camera equipped with a microphone and speaker is meant for communication for the elderly for sending and receiving voice commands remotely. The camera can be accessed through an IoT environment which can be communicated on remote IP addresses on smartphones and personal computers. There are two Arduino Uno Rev3 Controller modules programmed to control the gestures of the gyroscopic accelerometer and other control methods based on a mode selection switch held by the user. The proposed methodology is also realized by Proteus, a simulation tool to check its practical credibility. The proposed circuitry is physically realized as a Smart Wheelchair prototype implemented with the above multi control which could be modified as any physical structure platform having two wheels.