To know a person's Hearing Loss, generally we use a Pure Tone Audiometer. To use this it requires a bulk of the instrument to be carried. This testing requires a considerable amount of time for testing. It was observed that a large level of hearing testing, it is consuming much time. So a hand-held Mobile phone operated APP BASED AUDIOMETER will be of immense useful in finding and detecting hearing loss in children in schools

Introducing an app-based audiometer for school screenings revolutionizes the way we detect and address hearing loss in children. This mobile phone-operated solution offers unparalleled accessibility, convenience, and cost-effectiveness, drastically reducing testing time while ensuring early detection. By leveraging modern technology, this app empowers healthcare providers, educators, and parents to swiftly identify potential hearing issues, enabling timely intervention and fostering better academic and social outcomes for children.

• Pure Tone Audiometry Test
• Results, Analysis, and Interpretation of Test
• Classroom Based management system with Analytics
• Connect to the Nearest Doctor
• Digitization of physical audiometry documents
• and many more...

• React Native
• Django

• Create a Firebase account
• Add the firebase config in FirebaseConfig.js in audiometer/

• Clone the repository
• Run the below commands:

cd audiometer
npm install
npm run web

• Change ip value to address in the command line in the file audiometer/app/Constants/ip.js

• Run the below commands:

cd server 

• Create Virtual Environment

You can use multiple methods to create a virtual environment, whatever suits your need

conda create -n venv python=3.6.3 anaconda
conda activate venv

python -m venv venv   
.\venv\Scripts\Activate

python3 -m venv venv 
source venv/bin/activate

• Install the requirements and run server

pip install -r requirements.txt
#Intialize DB data/Create Models in DB
python manage.py makemigrations
python manage.py migrate

cd backend
python manage.py runserver <ip_address>:80/<ip_address>:8081 

#Here <ip_address> is the IP address on which the expo is running

Team Members

• Sachin Kumar Sahu
• Palivela Ganesh Priyatham
• Preethi Varsha Marivina
• Siddhartha G
• Swami Ramchandra Kedari
• Sirish Sekhar

• Charih, François, and James R. Green. "Audiogram Digitization Tool for Audiological Reports." IEEE Access 10 (2022): 110761-110769.
• Machine Learning in Audiology: Applications and Implications.