Résumé
This present work provides an easily disposable paper-based interdigitated sensor embedded inside the mask for the detection of respiration rate continuously without direct contact with the human body. In present COVID scenario, everyone needs to wear mask for preventing the unwanted spreading of the said virus. But in tropical countries like India, People always complain about a suffocating situation while wearing mask. In order to address this, we have designed a non-invasive paper sensor that can be placed inside the mask (beneath nostril) for measuring breathing rate regularly to find out a cost-effective device capable of monitoring breathing rate. In general, we prefer use and throw type mask, thus the proposed paper-based sensor gives a cost-effective solution compared to the Current appliances of breathing sensing as most of them are burdensome, bulky, inconvenient as well as exorbitant than a paper-based respiration sensor. Essentially, these current approaches undergo through a practical limitation of frequency and time span i.e. static measurement. It is known that the humidity level of a paper changes according to the moisture content. Thus, it can easily detect the inhalation and exhalation through this changing. Moreover, variation of moisture level that happens due to breathing cycle changes the ionic conductivity of the sensor. The interdigitated design is the most suited for achieving higher sensitivity and easy to fabricate. First, we have designed it in Finite Element Method (FEM) based platf orm and finally this design is implemented with graphite-based electrodes on cellulose paper. Microcontroller based electronic circuitry is used in order to monitor and record the real-time respiration rate. It has been observed that the breathing rate as well as pattern gives a significant variation along with physical conditions and obstructive pulmonary diseases.
Résumé
The recent explosion of interest in online courses can directly be attributed to the current pandemic which has collated to the adoption of Massive Open Online Courses. While the enforced social distancing protocol inevitably demands the involvement of technology in raising awareness, it further requires the imposition of preventive restrictions on touch-based systems. Visual gestures like simple blinks can account for a significant subset of possible actions in mobile application domains. The proposed model introduces a novel approach that facilitates the automated generation of relevant lecture notes and web-linked keywords from videos through a blink-controlled interface. Functions like auto-video pausing and auto-closing of the application based on eye attribute tracking have been integrated into the system for handling context switching. The experimental results reveal 84.17 usability score, an average of 89.55% of different types of blink detection accuracy, 91.56% of text generation accuracy and 70.41% of keyword detection accuracy along with low false positives and false negatives under different lighting conditions. © 2021, The Institution of Engineers (India).