Featured Project

# Team Members

- Ahmed Nahas (anahas2)

- Siraj Khogeer (khogeer2)

- Abdulrahman Maaieh (amaaieh2)

# Problem:

The underlying motive behind this project is the heart-wrenching fact that, with all the developments in science and technology, the visually impaired have been left with nothing but a simple white cane; a stick among today’s scientific novelties. Our overarching goal is to create a wearable assistive device for the visually impaired by giving them an alternative way of “seeing” through sound. The idea revolves around glasses/headset that allow the user to walk independently by detecting obstacles and notifying the user, creating a sense of vision through spatial awareness.

# Solution:

Our objective is to create smart glasses/headset that allow the visually impaired to ‘see’ through sound. The general idea is to map the user’s surroundings through depth maps and a normal camera, then map both to audio that allows the user to perceive their surroundings.

We’ll use two low-power I2C ToF imagers to build a depth map of the user’s surroundings, as well as an SPI camera for ML features such as object recognition. These cameras/imagers will be connected to our ESP32-S3 WROOM, which downsamples some of the input and offloads them to our phone app/webpage for heavier processing (for object recognition, as well as for the depth-map to sound algorithm, which will be quite complex and builds on research papers we’ve found).

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# Subsystems:

## Subsystem 1: Microcontroller Unit

We will use an ESP as an MCU, mainly for its WIFI capabilities as well as its sufficient processing power, suitable for us to connect

- ESP32-S3 WROOM : https://www.digikey.com/en/products/detail/espressif-systems/ESP32-S3-WROOM-1-N8/15200089

## Subsystem 2: Tof Depth Imagers/Cameras Subsystem

This subsystem is the main sensor subsystem for getting the depth map data. This data will be transformed into audio signals to allow a visually impaired person to perceive obstacles around them.

There will be two Tof sensors to provide a wide FOV which will be connected to the ESP-32 MCU through two I2C connections. Each sensor provides a 8x8 pixel array at a 63 degree FOV.

- x2 SparkFun Qwiic Mini ToF Imager - VL53L5CX: https://www.sparkfun.com/products/19013

## Subsystem 3: SPI Camera Subsystem

This subsystem will allow us to capture a colored image of the user’s surroundings. A captured image will allow us to implement egocentric computer vision, processed on the app. We will implement one ML feature as a baseline for this project (one of: scene description, object recognition, etc). This will only be given as feedback to the user once prompted by a button on the PCB: when the user clicks the button on the glasses/headset, they will hear a description of their surroundings (hence, we don’t need real time object recognition, as opposed to a higher frame rate for the depth maps which do need lower latency. So as low as 1fps is what we need). This is exciting as having such an input will allow for other ML features/integrations that can be scaled drastically beyond this course.

- x1 Mega 3MP SPI Camera Module: https://www.arducam.com/product/presale-mega-3mp-color-rolling-shutter-camera-module-with-solid-camera-case-for-any-microcontroller/

## Subsystem 4: Stereo Audio Circuit

This subsystem is in charge of converting the digital audio from the ESP-32 and APP into stereo output to be used with earphones or speakers. This included digital to audio conversion and voltage clamping/regulation. Potentially add an adjustable audio option through a potentiometer.

- DAC Circuit

- 2*Op-Amp for Stereo Output, TLC27L1ACP:https://www.ti.com/product/TLC27L1A/part-details/TLC27L1ACP

- SJ1-3554NG (AUX)

- Connection to speakers/earphones https://www.digikey.com/en/products/detail/cui-devices/SJ1-3554NG/738709

- Bone conduction Transducer (optional, to be tested)

- Will allow for a bone conduction audio output, easily integrated around the ear in place of earphones, to be tested for effectiveness. Replaced with earphones otherwise. https://www.adafruit.com/product/1674

## Subsystem 5: App Subsystem

- React Native App/webpage, connects directly to ESP

- Does the heavy processing for the spatial awareness algorithm as well as object recognition or scene description algorithms (using libraries such as yolo, opencv, tflite)

- Sends audio output back to ESP to be outputted to stereo audio circuit

## Subsystem 6: Battery and Power Management

This subsystem is in charge of Power delivery, voltage regulation, and battery management to the rest of the circuit and devices. Takes in the unregulated battery voltage and steps up or down according to each components needs

- Main Power Supply

- Lithium Ion Battery Pack

- Voltage Regulators

- Linear, Buck, Boost regulators for the MCU, Sensors, and DAC

- Enclosure and Routing

- Plastic enclosure for the battery pack

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# Criterion for Success

**Obstacle Detection:**

- Be able to identify the difference between an obstacle that is 1 meter away vs an obstacle that is 3 meters away.

- Be able to differentiate between obstacles on the right vs the left side of the user

- Be able to perceive an object moving from left to right or right to left in front of the user

**MCU:**

- Offload data from sensor subsystems onto application through a wifi connection.

- Control and receive data from sensors (ToF imagers and SPI camera) using SPI and I2C

- Receive audio from application and pass onto DAC for stereo out.

**App/Webpage:**

- Successfully connects to ESP through WIFI or BLE

- Processes data (ML and depth map algorithms)

- Process image using ML for object recognition

- Transforms depth map into spatial audio

- Sends audio back to ESP for audio output

**Audio:**

- Have working stereo output on the PCB for use in wired earphones or built in speakers

- Have bluetooth working on the app if a user wants to use wireless audio

- Potentially add hardware volume control

**Power:**

- Be able to operate the device using battery power. Safe voltage levels and regulation are needed.

- 5.5V Max

Project Videos

• BlindVision Pro - Team 38