Project Hub /Artificial Intelligence & Edge ML/Object Detection & Tracking/TheAssitBot : Wearable AI Assistant for the Deaf and Mute

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MAIXDUINO

₹5,000 – ₹15,000

6 – 12 Hours

Partial Kit

TheAssitBot : Wearable AI Assistant for the Deaf and Mute

A wearable, AI-driven assistant designed for real-time sign language recognition, speech-to-text translation, and object detection using a compact embedded board.

Originally published on CircuitDigest

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.

ESP32 CAM WiFi Module Bluetooth with OV2640 Camera Rs. 810.00
Microphone module - Analog Sound Sensor Module for Arduino Rs. 63.00
Cable for Arduino Nano (USB 2.0 A to USB 2.0 Mini B) 30cm Rs. 69.00
2-Slot 18650 Holder with Protective Cover and On/Off Switch – Case Rs. 109.00
10pcs 5mm RED Diffused Light Emitting Diodes - LEDs Set Rs. 39.00
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 Male 20cm Rs. 45.00
Jumper Wire 10 Pin Male To Female 20cm Rs. 45.00

Selected: Rs. 0.00

We don't carry yet: Prototype Kit / Sensor Kit / Starter Kit
Other Dev Board (Indusboard / Maixduino / misc)
TFT / Touch LCD
AA / AAA Battery + Holder
SD / MicroSD Card
Speaker / Audio Amplifier
Enclosure / Box / Case

Browse all components

How It Works

TheAssitBot operates as an edge-powered wearable device centered around the Sipeed Maixduino AI board, which handles local machine learning processing without needing an active cloud connection. Visual data is captured by the attached camera module, processed using TensorFlow-based models running within the MaixPy framework, and displayed visually on the 2.4-inch TFT screen or output audibly through the speaker. For audio input, the onboard microphone captures spoken words for speech-to-text conversion, while external data like trained neural network models can be stored on the external SD card. User interaction is handled via physical push buttons and status LEDs, allowing operators to trigger specific modes or view feedback directly. Power is delivered reliably to the entire system via a dual 18650 LiPo battery pack, all securely housed inside a custom 3D-printed enclosure and charged or programmed using a USB Type-C cable. Finally, the entire assembly can be comfortably worn using the included velcro straps.

Why Build This

Design and deploy custom edge machine learning models for real-time sign language and speech translation.

Integrate multiple hardware peripherals—such as cameras, microphones, and displays—into a portable, wearable embedded system.

Develop assistive technology applications that directly address real-world communication barriers for deaf and mute individuals.

Real-World Application

Bridges communication gaps for individuals who are deaf or mute by translating sign language into text or speech and converting spoken words into readable text.

Skills You'll Learn

Edge AI deployment

computer vision integration

embedded Python scripting

audio processing

hardware enclosure design

power management with lithium-ion cells

Safety Precautions

Handle lithium-ion batteries with care to avoid short circuits or overcharging.

Technology Tags

IOT

EDGE AI

SIGN LANGUAGE RECOGNITION

SPEECH-TO-TEXT

OBJECT DETECTION

HAND GESTURE RECOGNITION

MACHINE LEARNING

TENSORFLOW

PYTHON

MAIXPY

EMBEDDED SYSTEMS

Frequently Asked Questions

Does this wearable require an internet connection to translate sign language or speech?

No, all object detection and machine learning inferences run locally on the Sipeed Maixduino board at the edge, ensuring privacy and offline functionality.

Can I use standard alkaline batteries instead of the 18650 LiPo cell pack?

No, the power architecture is designed around rechargeable 18650 lithium-ion cells to provide the necessary voltage, current draw, and rechargeable portability required for a wearable device.

How do I load custom AI models onto the device?

Trained TensorFlow models and scripts are developed using Jupyter Notebook and Python, then loaded onto the system using the external SD card or USB Type-C connection.

Gallery

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-01112