Inspiration
What if losing the ability to use a hand didn't mean losing the ability to interact with the world?
This question inspired me to create NEUROHAND AI, an intelligent assistive technology designed to improve independence and communication for individuals with limb disabilities, including those who face difficulties with verbal communication.
I wanted to explore how Artificial Intelligence, biosignals, robotics, and human-computer interaction could come together to solve real-world accessibility challenges. Instead of creating just another robotic hand, I envisioned a system that could eventually understand human intentions, assist with everyday activities, and bridge communication gaps.
What it does
NEUROHAND AI is an AI-powered assistive system combining a robotic hand, biosignal acquisition, and an intelligent mobile application.
Its key features include:
- Robotic Hand: An ESP32-controlled robotic hand with servo motors that enables individual finger movements and basic gripping experiments.
- BioSignal Interface: Uses the BioAmp EXG Pill to explore the acquisition and processing of electrical signals from the human body, with the long-term goal of enabling hands-free robotic control.
- AI-Powered Sign Language Interpretation: Uses Google's Gemini AI for image-based gesture analysis and text translation.
- Health Monitoring: Designed to incorporate physiological sensors for monitoring relevant health parameters.
- AI Assistant: Provides an intelligent assistance interface.
- Camera and OCR: Designed to support visual information recognition and text extraction.
- Emergency Assistance: Includes planned SOS and GPS-based emergency support.
- Medical History: A dedicated section for maintaining relevant medical information.
How we built it
I developed NEUROHAND AI by combining hardware experimentation with software development.
For the hardware, I used an ESP32 microcontroller and servo motors to construct and control the robotic hand. I experimented with motor positioning, finger movements, and gripping mechanisms.
I also integrated the BioAmp EXG Pill to begin exploring biosignal acquisition and feature extraction. My objective is to investigate how meaningful signal patterns can eventually be translated into robotic commands.
For the software, I developed a mobile application using Flutter. I integrated Google's Gemini AI for image-based sign-language interpretation and text translation.
The application is designed around multiple modules, including Dashboard, Robotic Hand, Health, AI Assistant, Camera, Medical History, Emergency, and Settings.
Challenges we ran into
Developing NEUROHAND AI independently involved several technical challenges.
- Biosignal Acquisition: Understanding how to obtain stable signals from
Built With
- arduinoide
- dart
- esp32
- exgpill
- flutter
- lcddisplay
- vscode
