Highlights Early Identification or Prediction of COVID-19 cases. Real-time Monitoring of COVID-19. Treatment Response of COVID-19 confirmed cases. An IoT-based Framework for COVID-19.

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Cardiovascular Disease (CVD) is one of the most catastrophic and life threatening health issue nowadays. Early detection of CVD is an important solution to reduce its devastating effects on health. In this paper, an efficient CVD detection algorithm is identified. The algorithm uses patient demographic data as inputs, along with several ECG signal features extracted automatically through signal processing techniques. Cross-validation results show a 98.29 % accuracy for the decision tree classification algorithm. The algorithm has been integrated into a web based system that can be used at anytime by patients to check their heart health status. At one end of the system is the ECG sensor attached to the patient's body, while at the other end is the detection algorithm. Communication between the two ends is done through an Android application.

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Sign language can be used to facilitate communication with and between deaf or hard of hearing (Deaf/HH). With the advent of video streaming applications in smart TVs and mobile devices, it is now possible to use sign language to communicate over worldwide networks. In this article, we develop a prototype assistive device for real-time speech-to-sign translation. The proposed device aims at enabling Deaf/HH people to access and understand materials delivered in mobile streaming videos through the applications of pipelined and parallel processing for real-time translation, and the application of eye-tracking based user-satisfaction detection to support dynamic learning to improve speech-to-signing translation. We conduct two experiments to evaluate the performance and usability of the proposed assistive device. Nine deaf people participated in these experiments. Our real-time performance evaluation shows the addition of viewer's attention-based feedback reduced translation error rates by 16% (per the sign error rate [SER] metric) and increased translation accuracy by 5.4% (per the bilingual evaluation understudy [BLEU] metric) when compared to a non-real-time baseline system without these features. The usability study results indicate that our assistive device was also pleasant and satisfying to deaf users, and it may contribute to greater engagement of deaf people in day-to-day activities.