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IoT Activity Through Eye Control Things Just By Looking

Advanced

RASPBERRY PI

Project Specific

3 – 6 Hours

Partial Kit

IoT Activity Through Eye Control Things Just By Looking

Control home appliances by simply looking at them using a Raspberry Pi, camera, and TensorFlow-based object detection.

Originally published on ElectronicsForU

View original tutorial

Parts You'll Need

This project is sourced from another site, not written by us — this list is our best match of their components against what we stock, not a verified bill of materials. Always check the original blog (linked above) for the actual parts list before buying.

Raspberry Pi 3 - Model B+ 1.4Ghz 64-bit quad-core Processor with 1 GB Ram (Original) Rs. 9,810.00
5V 1 Channel Relay Module Rs. 87.00
Jumper Wire 10 Pin Male To Male 20cm Rs. 45.00
400 Points Mini Solderless Prototype PCB Breadboard High Quality Rs. 89.00
MB102 Adjustable Breadboard Power Supply Module (3.3V & 5V) for Arduino & Raspberry Pi Rs. 139.00
Jumper Wire 10 Pin Male To Female 20cm Rs. 45.00

Selected: Rs. 0.00

We don't carry yet: Battery (generic / unspecified)
Eyeglasses

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How It Works

The IoT Activity Through Eye Control system operates by capturing visual input through a Raspberry Pi Camera mounted on a pair of eyeglasses, which feeds real-time video frames into a Raspberry Pi 5. Processing these frames relies on Python utilizing OpenCV for image handling alongside TensorFlow and Keras to run deep learning models capable of object and gaze detection. When the system recognizes a specific focal target or visual intent associated with a user's gaze direction, the software triggers a designated output pin. This pin interfaces with a relay module using the gpiozero library, sending a low-voltage control signal to switch the relay state. Because the system is entirely portable, it draws power from a compact USB power bank connected to the Raspberry Pi. Through this integrated pipeline of machine learning inference, general-purpose input/output pin manipulation, and electromechanical relay switching, the user can successfully interface with external electronic loads hands-free.

Why Build This

Develop practical assistive technologies that empower specially abled individuals to independently control household appliances using only eye movements.

Implement advanced computer vision and deep learning pipelines on an edge computing platform like the Raspberry Pi 5.

Gain hands-on engineering experience bridging high-level software classification models with physical hardware controls via relays.

Real-World Application

Assistive technology for specially abled individuals to independently operate home appliances, and hands-free smart home automation.

Skills You'll Learn

Computer vision processing

Deep learning inference

Neural network implementation

General-purpose input/output pin control

Electromechanical relay interfacing

Embedded Python programming

Safety Precautions

Exercise caution when working with mains voltage relays and appliances to avoid electrical shock.

Technology Tags

PYTHON

OPENCV

TENSORFLOW

KERAS

GPIOZERO

IOT

EYE TRACKING

OBJECT DETECTION

RELAY

CAMERA

Frequently Asked Questions

Do I need a GPU to run TensorFlow and Keras for this project?

No, the Raspberry Pi 5 has sufficient processing capability to handle lightweight inference models, though optimization using frameworks like TensorFlow Lite can improve frame rates.

How does the camera on the eyeglasses connect to the Raspberry Pi?

The Raspberry Pi Camera connects directly via a dedicated camera serial interface ribbon cable or a compatible USB connection depending on the exact camera module used.

Can any home appliance be controlled by this system?

Yes, provided the appliance is safely wired through the relay module, allowing the low-voltage Raspberry Pi output to switch higher-voltage circuits.

This page is part of Kitkraft's Project Hub — a curated collection of electronics & robotics projects gathered from the maker community to help students and hobbyists discover and learn to build. Kitkraft does not claim authorship of the original project, and makes no representation as to the accuracy, completeness or safety of third-party source material. The summary and "How It Works" explanation above are AI-generated, rewritten with reference to the original tutorial — not a reproduction of it. Full credit and a direct link to the original source are provided above. If you're the original creator and have a concern about how your work is represented here, please contact us and we'll address it promptly.

Kitkraft Project Hub — curated from the maker community, credited at the source.

PRJ-00355