Interfacing Electrocardiogram (ECG) with IoT

Interfacing Electrocardiogram (ECG) with IoT

A Telehealth system that offers remote monitoring of the heart condition

Internet of Things

  • 0 Collaborators



A low-cost Electrocardiogram circuit is designed using Instrumentation amplifiers, band-pass filters, electrodes and signal sampling circuit for digital interfacing. The ECG employs 12-leads to monitor the heart rate and plot the rhythm on a computer. Though the readings are localised at the hospital or health centre, Internet of Things (IoT) would be incorporated to cloud the readings for remote monitoring. Mobile applications will be developed to reproduce the heart rhythm that was generated at the local station.

Critical heart conditions can be remotely shared with a specialist from around the world to proffer medical expertise. For example, a doctor in America or India can monitor the heart condition of a patient in Lagos (real-time).

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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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