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LOVELY PROFESSIONAL UNIVERSITY,PHAGWARA,PUNJAB,INDIA
Facial emotion recognition (FER) is the procedure of identifying human emotions from facial expressions. It is often difficult to identify the stress and anxiety levels of an individual through the visuals captured from computer vision. However, the technology enhancements on the Internet of Medical Things (IoMT) have yielded impressive results from gathering various forms of emotional and physical health-related data. The novel deep learning (DL) algorithms are allowing to perform application in a resource-constrained edge environment, encouraging data from IoMT devices to be processed locally at the edge. This article presents an IoMT based facial emotion detection and recognition system that has been implemented in real-time by utilizing a small, powerful, and resource-constrained device known as Raspberry-Pi with the assistance of deep convolution neural networks. For this purpose, we have conducted one empirical study on the facial emotions of human beings along with the emotional state of human beings using physiological sensors. It then proposes a model for the detection of emotions in real-time on a resource-constrained device, i.e., Raspberry-Pi, along with a co-processor, i.e., Intel Movidius NCS2. The facial emotion detection test accuracy ranged from 56% to 73% using various models, and the accuracy has become 73% performed very well with the FER 2013 dataset in comparison to the state of art results mentioned as 64% maximum. A t-test is performed for extracting the significant difference in systolic, diastolic blood pressure, and the heart rate of an individual watching three different subjects (angry, happy, and neutral).
Navjot Rathour; Sultan Alshamrani; Rajesh Singh; Anita Gehlot; Mamoon Rashid; Shaik Akram; Ahmed AlGhamdi. IoMT Based Facial Emotion Recognition System Using Deep Convolution Neural Networks. Electronics 2021, 10, 1289 .
AMA StyleNavjot Rathour, Sultan Alshamrani, Rajesh Singh, Anita Gehlot, Mamoon Rashid, Shaik Akram, Ahmed AlGhamdi. IoMT Based Facial Emotion Recognition System Using Deep Convolution Neural Networks. Electronics. 2021; 10 (11):1289.
Chicago/Turabian StyleNavjot Rathour; Sultan Alshamrani; Rajesh Singh; Anita Gehlot; Mamoon Rashid; Shaik Akram; Ahmed AlGhamdi. 2021. "IoMT Based Facial Emotion Recognition System Using Deep Convolution Neural Networks." Electronics 10, no. 11: 1289.
Hand sanitation acquiescence is immensely essential in hospitals, clinics and in the sphere of food industry. Caregiver’s docility with hand sanitation in the most adequate mechanism is mandate for restraining healthcare-associated infections (HAIs) in hospitals and clinics. Washing hands legitimately is the foundation of hand sanitation. Notwithstanding, the hand wash consistence rate by the labourers (parental figures, servers, gourmet experts, food processors, etc.) is not up to the mark. Observing hand wash consistence alongside an updated framework expands the consistence rate essentially. Quality of hand sanitation is also important, which can be accomplished by washing hands as per standard rules and guidelines. In this paper, we present Euphony, a hand sanitation monitoring and indication system, that screens hand sanitation occasions and their quality, gives continuous input, helps the individual to remember intrigue when he/she is required to sanitize hands, and stores related information on a server for further use. Euphony is vigorous, versatile and simple to introduce, and it conquers a large portion of the issues of existing related frameworks.
Navjot Rathour; Rajesh Singh; Anita Gehlot. Image and Video Capturing for Proper Hand Sanitation Surveillance in Hospitals Using Euphony—A Raspberry Pi and Arduino-Based Device. Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences 2019, 1475 -1486.
AMA StyleNavjot Rathour, Rajesh Singh, Anita Gehlot. Image and Video Capturing for Proper Hand Sanitation Surveillance in Hospitals Using Euphony—A Raspberry Pi and Arduino-Based Device. Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences. 2019; ():1475-1486.
Chicago/Turabian StyleNavjot Rathour; Rajesh Singh; Anita Gehlot. 2019. "Image and Video Capturing for Proper Hand Sanitation Surveillance in Hospitals Using Euphony—A Raspberry Pi and Arduino-Based Device." Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences , no. : 1475-1486.
To augment the performance of the PTL AND gate compared with that of basic AND gate cell with the need of low power and delay which cause the advancement of uncompromising designing approaches to bring down the power consumption significantly. To endure the rising demand, we put forward a low power PTL AND gate cell via forfeit the MOS transistor reckon that shrink the severe threshold loss drawback, considerably improve the speed and drop off the power while relate with the static energy recovery of AND gate when used in adder circuit. The present work links 1.8V, 1.7V, 1.6V, 1.5V, 1.4V, 1.3V, 1.2V, 1.1V, 1.0V MOS transistor PTL AND gate cells in assessment of basic AND gate described in the research literature. We have been worked out for power, delay and power delay product values of all the cells using 0.09μm technology.
Remalli Dinesh; Sandeep Bansal; Cherry Bhargava; Navjot Rathour; Raghav Gupta. A Low-Density Power and Delay Testing of PTL and Gate Using 0.09µm Technology. 2018 International Conference on Intelligent Circuits and Systems (ICICS) 2018, 69 -71.
AMA StyleRemalli Dinesh, Sandeep Bansal, Cherry Bhargava, Navjot Rathour, Raghav Gupta. A Low-Density Power and Delay Testing of PTL and Gate Using 0.09µm Technology. 2018 International Conference on Intelligent Circuits and Systems (ICICS). 2018; ():69-71.
Chicago/Turabian StyleRemalli Dinesh; Sandeep Bansal; Cherry Bhargava; Navjot Rathour; Raghav Gupta. 2018. "A Low-Density Power and Delay Testing of PTL and Gate Using 0.09µm Technology." 2018 International Conference on Intelligent Circuits and Systems (ICICS) , no. : 69-71.
A conventional N-bit flash analog to digital converter has been required the 2 N number of resistors and 2 N -1 number of preamplifiers as well as comparators. In this proposed work, a number of comparators could be reduced by introducing the multiplexer (MUX). This proposed work has only required the (2 (N-2) + 1) number of comparators. For 6-bit resolution, MUX based flash ADC requires a reduced number of comparators by 73%, respectively, compared with the traditional flash ADC. This proposed 6-bit ADC consists of a reference ladder circuit, a (2 × 1) multiplexer, 8 (4 × 1) multiplexer, 17 comparators and thermometer to binary encoder. The proposed 6-bit 200 MSPS ADC is designed and simulated in cadence tools with 1 V supply voltage using 90nm CMOS technology. The proposed work results into effective number of bits (ENOB) of 5.69 bit and figure of merit (FOM) of 0.019 pJ/conversion-step for 200 MS/s.
Vivek Modi; Cherry Bhargava; Navjot Rathour; Sandeep Bansal. MUX Based Flash ADC for Reduction in Number of Comparators. 2018 International Conference on Intelligent Circuits and Systems (ICICS) 2018, 52 -57.
AMA StyleVivek Modi, Cherry Bhargava, Navjot Rathour, Sandeep Bansal. MUX Based Flash ADC for Reduction in Number of Comparators. 2018 International Conference on Intelligent Circuits and Systems (ICICS). 2018; ():52-57.
Chicago/Turabian StyleVivek Modi; Cherry Bhargava; Navjot Rathour; Sandeep Bansal. 2018. "MUX Based Flash ADC for Reduction in Number of Comparators." 2018 International Conference on Intelligent Circuits and Systems (ICICS) , no. : 52-57.