Project Info

DigitalTwin

This project did not submit a demo video on Devpost.

Inspiration

: Digital Twin was born from a simple yet powerful idea: What if a person’s cough could tell the story of their respiratory health? In a world shaped by remote care and digital diagnostics, we envisioned a tool that could democratize access to early respiratory disease detection by making it affordable, user-friendly, and non-invasive. We were inspired by the opportunity to reduce diagnostic delays and empower communities with proactive, accessible healthcare.

What it does

: Digital Twin is a web-based application that allows users to record their cough and receive a prediction of potential respiratory illness. It leverages a machine learning model trained on cough audio data and integrates Vapi to enable real-time voice interaction. The output provides a preliminary classification to support early screening and awareness.

How we built it

: After extensive planning and discussion, we divided our workflow into specialized components: Built and trained a machine learning model using labeled cough sound data Integrated the Vapi voice API for voice input capture and backend processing Designed a responsive web application interface

Challenges we ran into

: Difficulty in finding a publicly available dataset of cough sounds from patients with respiratory diseases, which constrained model development and validation Unfamiliarity with Vapi's API and voice processing tools, which required time to understand and implement effectively Limited frontend development experience, especially in deploying interactive and responsive web interfaces To overcome these we: Participated in workshops and studied documentation Conducted frequent internal check-ins and collaborative debugging Supported one another across roles to close skill gaps

Accomplishments we're proud of

: Developed an interactive web platform from scratch, despite limited prior frontend experience Integrated a voice-based interface for health screening, enhancing accessibility and user experience

What we learned

: Acoustic data can serve as powerful digital biomarkers when paired with appropriate ML architectures AI tooling can provide a low cost alternatives for remote settings where resourced are limited while providing many of the services which would require a team to do like customer service and support, web deployment, and resource allocation Collaboration, communication, and flexibility are critical in overcoming steep learning curves

What's next

: Expand the dataset to include more real world data in order improve the quality of predictions as well as generalize across to more respiratory illnesses Deploy on mobile platforms for increasing accessibility to limited resource settings Expand regulatory compliance framework across other countries Generalize platform to include other longitudinal monitoring of health (ie diabetic retinopathy)

👥 Digital Twin

Digital Twin is a machine learning-powered platform leveraging cough acoustics for non-invasive, continuous respiratory disease screening.

VAPI Audio Analysis Client

This Python application provides a simple interface to interact with the VAPI API for audio analysis.

Setup

Install dependencies:

pip install -r requirements.txt

Create a .env file in the project root with your API key:

VAPI_API_KEY=your_api_key_here

Usage

The application provides a simple interface to analyze audio files. Here's how to use it:

from vapi_client import VAPIClient

# Initialize the client
client = VAPIClient()

# Analyze an audio file
analysis = client.analyze_audio("path/to/your/audio/file.wav")

# Save the results
if analysis:
    client.save_analysis_results(analysis, "audio_analysis.json")

Features

Analyze audio files using the VAPI API
Save analysis results to JSON files
Error handling and file management

PNG image

Requirements

Python 3.7+
Required packages (see requirements.txt):
- requests
- python-dotenv

Analysis

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HTMLIn code
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Codebase size

Source size

53 KB

Source files

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