Environmental Analyzer for Migraines

Environmental Analyzer for Migraines

Design of an IoT sensor device and application for reading room data to analyze migraines in terms of the environment using AWS.

Internet of Things

  • 0 Collaborators



The environmental analyzer for migraines is designed to take in environmental data from a user’s surroundings, i.e. temperature, humidity, pressure, altitude and light, while also taking in user data about migraines. These two datasets are then combined and utilized to define a user’s common environment and migraine occurrences. Once the data has been collected, an iOS app analyzes room data variables, such as temperature and pressure, against a user’s migraine data to examine migraines in terms of the environment in which the user was living providing users with both a visual and numerical representation of the data. The data collected for migraines included type, severity, date and time of each episode. The project is designed with the user in mind to help those suffering from migraines to better understand what environmental impacts are affecting them and what needs to be done in their environments to live a healthier life style.

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Rima M. updated status

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Rima Modak

I am currently working to develop a prototype of an device based on IoT. This device will be used to detect any kind of material. It will be used to detect both living and non-living things, and after detecting, it will display varies other things related to the detected object according to the tags provided by the user/customer.

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Aravindhan N. created project Automatic attendance management system using face detection

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Automatic attendance management system using face detection

Automatic attendance management system will replace the manual method, which takes a lot of time and is difficult to maintain.There are many bio metric processes ,in that face recognition is the best method. In our campus staff attendance is taken with the help of Gesture recognition /attendance sheet .We can take this to next level by implementing Artificial Intelligence based Face Recognition using Convolution Neural Network(CNN). We have to train our neural net using COCO (large Image dataset designed for object detection) and Staff Dataset (Several images of individual staffs). Since we don't have the photos of the staffs,we have trained our neural net using our own photos.Our Neural net consists of 20 neurons in the hidden layer which help us to diagnose the pixels of the image and compares the result with the trained dataset .By using our advanced system the staffs can use their own mobile/laptop [camera] for registering their presence in their own place which is possible only if they are connected to our college Network (WiFi).

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Melisa M. updated status

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melisa Mendoza

I’m a Technical Collaborator at the Innovation Lab Network at my University, the Technical University of Queretaro (UTEQ). Here I work with a group of students and teachers to create solutions for real world problems by joining hardware and software technologies. We use databases like Mongo, Maria and SQL. I believe that the junction between Information technologies and hardware will create amazing solutions for the industry and my community, with the use of IoT.

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Mark M. updated status

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Mark Munavi

Android app that work with insurance companies and insurance broker that is used to authenticate people seeking services from medical service provider in developed countries.

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