Design of Gesture‐Based Hand Gloves Using Arduino UNO: A Grace to Abled Mankind

Bedi, Harpreet Singh; Raju, Dekkapati Vinit; Nandyala, Meghanath Reddy C.; Kumar, Partha Sai; Varma, Mandla Ravi

doi:10.1002/9781119769057.ch3

Cited by 3 publications

(3 citation statements)

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“…We can have multiple objects falling in the same grid. Multiple boxes of different shapes can be used to resolve the problem [17], each anchor box of a different shape being likely to detect an object of a shape For Every single picture in our video bytes, first way of doing/calculating identification of centroids. We can create a identical id to find the object, we have to find if there is any relation in the object centroid with old object centroid i.e yellow and purple dots Then if we want to calculate the centroids with less distance with consecutive pictures, then we can create an object.…”

Section: Anchor Boxesmentioning

confidence: 99%

Traffic Surveillance Using Computer Vision and Deep Learning Methods

Bedi,

Malik,

Singh

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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From the early 21st century to the present date, Traffic surveillance has been one of the most challenging problems and over the years many solutions were proposed for tackling this problem. However, flaws are can still be noticed in the currently existing systems for performing this task and some of them will be further addressed in this work. In our project Traffic Surveillance using Computer Vision and Deep Learning, we propose a smart vision-based system which accurately provides live traffic statistics like the speed of the traffic and the count of Vehicles passed on a road in between a time interval. The system takes a video feed as an input, which could be live footage provided by a CCTV camera at a traffic junction, and on querying starting and ending timestamps, it outputs the count of the vehicles and speed of the traffic in between the queried timestamps. We divide this problem into two major sub-problems, firstly detecting a vehicle, followed by tracking the detected vehicle. Although there exist many algorithms for detecting objects as well as for tracking objects, one of the major problems is integrating both. As ambiguities like detecting an already detected vehicle, many occur while tracking the vehicles. In our work, we emphasize handling such ambiguities using Deep Learning and we also provide a comparative performance analysis of our system with the already existing ones.

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Section: Anchor Boxesmentioning

confidence: 99%

Traffic Surveillance Using Computer Vision and Deep Learning Methods

Bedi,

Malik,

Singh

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…The literature also reveals that cardiovascular diseases affect a layer of young people from the age of 15 years and that the mortality rate is higher for the age intervals of [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], [30-44] and [75-plus] See Table I of this paper. The mortality rate from cardiovascular disease is expected to increase by 2030 according to the WHO.…”

Section: Related Workmentioning

confidence: 99%

“…It can supply up to 5 V to the components that connect to it. The Arduino Uno board is relatively large and has the disadvantage of using a USB connector to interface with computers [16].…”

Section: D) Arduino Unomentioning

confidence: 99%

Remote Monitoring Solution for Cardiovascular Diseases based on Internet of Things and PLX-DAQ add-in

BILONG¹,

BOSSOU²,

SIE³

et al. 2022

IJACSA

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Access to health remains a real problem in Africa especially for the follow up of patients with chronic diseases. Many cases of heart attack deaths are still recorded before victims can access treatment. This is due to several factors, namely the insufficient number of cardiologists, the inaccessibility of hospitals with adequate infrastructure, the carelessness and ignorance of people about their health. In response to these limitations, internet of Things, thanks to its remarkable technological contribution, allow to follow from afar and easily patient's condition. In this paper, we offer a ubiquitous surveillance solution distance from patients with cardiovascular disease in order to minimize or eliminate the risk of heart attacks. The proposed solution is based on a microservice architecture and consists of two essential parts that are data acquisition and data transfer. It will allow the patient to access their physical data and submit them in real time to the doctor through a dedicated medical application. The doctor will then be able to analyse the data obtained and return a prescription to the patient in case of abnormality .We used the microcontroller Arduino esp8066, the heart Rate Monitor AD8232 ECG (electrocardiogram) to measure the electrical activity of the heart that can be traced as an ECG, a pulse-eater, a photoresistor LDR and an potentiometer to regulate and modify the current flow in the circuit. We also used add-in PLX-DAQ for data acquisition and Jira software for data transfer to the doctor. Our solution is inexpensive and allows people not yet suffering from cardiovascular disease to prevent it.

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