Abstract

The idea for this project it to help to predict unsuccessful surgeries and warn professional staff about it. The final product will run during the surgeries. Our solution was to collect data from the surroundings, identify patterns and visualise the collected data and to alert users when the collected data exceeds ‘normal’ conditions. We have successfully created a sensor hub, which collects data and feeds it to an algorithm to calculate normal conditions, and a dashboard for users to visualise and interact with data.

Key Features

The Fusion Sensor Hardware will utilise a network of sensors that produce timestamped data in different rooms of a hospital. The data is to be uploaded to a backend to be analysed and archived for later inspection. The data received is to be used for learning of ‘normal’ behaviour and alert in case of abnormal data. The sensors can be easily added and removed to/from the current network of sensors or to create a new network of sensors.

Below is a short video which demonstrates these key features.

 

About Us

Information about who we are and our roles in the project.

Alexandros Frangos

alexandros.frangos.17@ucl.ac.uk

Team Leader, Hardware; In charge of developing the harware solution.

Ahmed Adeeb Fawzy

adeeb.fawzy.17@ucl.ac.uk

Back-end developer; In charge of creating the algorithm responsible for learning the 'normal'.

Nikolay Bortsov

nikolay.bortsov.17@ucl.ac.uk

Front-end developer; In charge of creating the Dashboard.