AEGLE: from the Greek mythology, the Goddess of health. It has meaning of "Brightness," or "Splendor," either from the beauty of the human body when in good health



By 2050, Indonesia is expected to have 72 million individuals aged 60 years and above. Indonesia is experiencing a ‘health transition' in which the most prevalent diseases among the elderly are chronic, non-infectious illnesses and injuries rather than acute infectious diseases. Currently, more than 35% of the Indonesian population suffers from 2 or more chronic illnesses; and 40% of deaths are due to chronic diseases such as diabetes & cardiovascular diseases.

The recent COVID-19 outbreak affects seniors and those with pre-existing chronic conditions the most, and these populations have a higher risk of developing complications & death. Vulnerable populations suffering from chronic illnesses required regular health check-in, however medical providers may not be able to reach them due to the current pandemic.


Inspired by the Portable Health Clinic (PHC) system, which take healthcare facilities along with remote doctors' consultancy to the doorsteps of the unreached people using an advanced telemedicine system, our idea is to connect patients who are suffering from chronic illnesses to medical providers by offering a two-way smart health monitoring platform. The platform collects accurate health data collection via wearable devices, facial and speech recognition; analyses data using trained machine learning algorithms; and presents the data in the form of data visualisation dashboard to medical providers. Thus, health monitoring for patients will be easy, simple, effective and more importantly trouble-free. Also, this will potentially reduce the pressure on hospitals and costs endured by patients, especially during pandemics.

What it does

Patients interface:

  • Health check - collect health data from facial features & wearable devices, using machine learning to analyze diseases and send result to doctor.

  • Health report - presen the analysed data in a simple way. It also helps users to track their historical health data

  • Call hotline - if patient felt unwell/ concerned about their health report, they can dial the hotline through the app to chat with a nurse

  • Wearables - search for & connect to wearable devices

Doctors interface:

Doctors have a list of patients and can check their condition everyday based on the data gathered by the App

  • Alert theshold system - doctors can set alert theshold according to patient's medical conditions & set reminders for prescriptions and/ or health check. When abnormal values are detected, the app the alert the doctor by highlighting the patient that required follow up

  • Data visualisation - show the data collected from facial recognition & wearable devices, medical conditions, prescriptions & personal information such as age/ gender/ allergies

How we built it

We made the low fidelity wireframes using Balsamiq, and created the high fidelity wireframes using Figma. We have developed the front-end for doctors interface using bubble and front-end for patients interface using Android Studio. The backend API was created using Node.js, Express, and PostgreSQL. And the Machine Learning model was train on google colab and has its own Flask API.

The Machine Learning model is used to detect eye diseases.

Challenges we ran into

It is our first time using bubble for front-end development, and we spend a lot of time of educating ourselves on the system and debugging.
Integrating our machine learning model, Backend, Frontend within a limited time. We had quite a few issues integrating Cotter for Android, and making sure that the machine learning model was working properly .

Accomplishments that we're proud of

We have completed the whole UI design, front-end and back-end development of the patient interface. We have trained the eyes disease classification model.

What we learned

Team management, Project management We learnt how to use Balsamiq to do quick low fidelity prototyping and use bubble for front-end development.

What's next for AEGLE

New Features

Speech analytics

Detect mental health illnesses via speech analytics

Video call

Users can call doctors for consultation


Users can order prescriptions via the app

Doctor lists

Users can look for doctors using the app and book appointments

Wearable device

We would like to develop our own wearable device to detect several health parameters.

Join us

Shoot us a message if you are interested!

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