Project

# Title Team Members TA Documents Sponsor
15 SafeStep: Smart White Cane Attachment for Audio + Haptic Navigation and Emergency Alerts
Abdulrahman Almana
Arsalan Ahmad
Eraad Ahmed
Abdullah Alawad design_document1.pdf
proposal1.pdf
# TEAM: Abdulrahman Almana (aalmana2), Arsalan Ahmed (aahma22), Eraad Ahmed (eahme2)

# PROBLEM
White canes provide reliable obstacle detection, but they do not give route-level navigation to help a user reach a destination efficiently. This can make it harder for blind or low-vision users to travel independently in unfamiliar areas. In addition, audio-only directions are not always accessible for users who are deaf or hard of hearing, and if a user falls there is often no automatic way to notify others quickly, which can delay assistance.
# SOLUTION OVERVIEW
We propose a modular smart attachment that mounts onto a standard white cane to improve navigation and safety without replacing the cane’s core purpose. The attachment will connect via Bluetooth to a user’s phone and headphones to support clear spoken directions, and it will also provide vibration-based cues for users who need non-audio feedback. The attachment will include fall detection and a basic emergency alert workflow that sends an alert to a pre-set emergency contact with the user’s last known location.
# SOLUTION COMPONENTS
**SUBSYSTEM 1, CONNECTIVITY + CONTROL**

Handles Bluetooth pairing, basic user controls, and system logic.

Planned Components:

1-ESP32 (Bluetooth Low Energy) microcontroller, ESP32-WROOM-32

2-Power switch + SOS button + cancel button

3-LiPo battery + USB-C charging module

**SUBSYSTEM 2, NAVIGATION OUTPUT (AUDIO + HAPTICS)**

Supports spoken directions through headphones and vibration cues for users who need non-audio feedback.

Planned Components:

1-Bluetooth connection to smartphone (using standard maps app audio)

2-Vibration motor (coin vibration motor, 3V) + motor driver (DRV8833)

3-Optional buzzer for confirmations

**SUBSYSTEM 3, LOCAL SENSING (WHEN MAPS NOT AVAILABLE)**

Provides short-range obstacle warnings and basic direction/heading feedback when GPS/maps are unreliable.

Planned Components:

1-Long-range distance sensor (Benewake TFmini-S LiDAR) for obstacle proximity alerts

2-IMU (MPU-9250) for motion/heading estimation

**SUBSYSTEM 4, FALL DETECTION + EMERGENCY ALERTING**

Detects falls and triggers an emergency workflow through the phone without a custom app.

Planned Components:

1-IMU-based fall detection using MPU-9250 data

2-BLE trigger to phone using standard phone shortcut automation

3-Phone sends SMS/call to pre-set emergency contact with last known GPS location

# CRITERION FOR SUCCESS

1-The attachment pairs to a smartphone and maintains a Bluetooth connection within 10 meters indoors.

2-The vibration system supports at least four distinct cues (left, right, straight, arrival).

3-The distance sensor detects obstacles within 20 cm to 12 m and triggers a warning vibration within 1 second.

4-Fall detection triggers within 5 seconds of a staged fall-like event and provides a cancel window (ex: 10 seconds).

5-When a fall is confirmed or SOS is pressed, the phone successfully notifies a designated contact and shares location (through phone shortcut automation).

6-The battery supports at least 1 hour of continuous operation.

# ALTERNATIVES

1-Smartphone-only navigation: Works for audio, but does not provide haptics for deaf/hard-of-hearing users and is not cane-integrated.

2-Smartwatch fall detection: Helps with emergencies but does not guide navigation through the cane.

3-Dedicated smart cane products: Often expensive and replace the cane instead of adding a modular attachment.

4-Wearable navigation (smart glasses): Higher cost and complexity.

Microcontroller-based Occupancy Monitoring (MOM)

Vish Gopal Sekar, John Li, Franklin Moy

Microcontroller-based Occupancy Monitoring (MOM)

Featured Project

# Microcontroller-based Occupancy Monitoring (MOM)

Team Members:

- Franklin Moy (fmoy3)

- Vish Gopal Sekar (vg12)

- John Li (johnwl2)

# Problem

With the campus returning to normalcy from the pandemic, most, if not all, students have returned to campus for the school year. This means that more and more students will be going to the libraries to study, which in turn means that the limited space at the libraries will be filled up with the many students who are now back on campus. Even in the semesters during the pandemic, many students have entered libraries such as Grainger to find study space, only to leave 5 minutes later because all of the seats are taken. This is definitely a loss not only to someone's study time, but maybe also their motivation to study at that point in time.

# Solution

We plan on utilizing a fleet of microcontrollers that will scan for nearby Wi-Fi and Bluetooth network signals in different areas of a building. Since students nowadays will be using phones and/or laptops that emit Wi-Fi and Bluetooth signals, scanning for Wi-Fi and Bluetooth signals is a good way to estimate the fullness of a building. Our microcontrollers, which will be deployed in numerous dedicated areas of a building (called sectors), will be able to detect these connections. The microcontrollers will then conduct some light processing to compile the fullness data for its sector. We will then feed this data into an IoT core in the cloud which will process and interpret the data and send it to a web app that will display this information in a user-friendly format.

# Solution Components

## Microcontrollers with Radio Antenna Suite

Each microcontroller will scan for Wi-Fi and Bluetooth packets in its vicinity, then it will compile this data for a set timeframe and send its findings to the IoT Core in the Cloud subsystem. Each microcontroller will be programmed with custom software that will interface with its different radio antennas, compile the data of detected signals, and send this data to the IoT Core in the Cloud subsystem.

The microcontroller that would suit the job would be the ESP32. It can be programmed to run a suite of real-time operating systems, which are perfect for IoT applications such as this one. This enables straightforward software development and easy connectivity with our IoT Core in the Cloud. The ESP32 also comes equipped with a 2.4 GHz Wi-Fi transceiver, which will be used to connect to the IoT Core, and a Bluetooth Low Energy transceiver, which will be part of the radio antenna suite.

Most UIUC Wi-Fi access points are dual-band, meaning that they communicate using both the 2.4 GHz and 5 GHz frequencies. Because of this, we will need to connect a separate dual-band antenna to the ESP32. The simplest solution is to get a USB dual-band Wi-Fi transceiver, such as the TP-Link Nano AC600, and plug it into a USB Type-A breakout board that we will connect to each ESP32's GPIO pins. Our custom software will interface with the USB Wi-Fi transceiver to scan for Wi-Fi activity, while it will use the ESP32's own Bluetooth Low Energy transceiver to scan for Bluetooth activity.

## Battery Backup

It is possible that the power supply to a microcontroller could fail, either due to a faulty power supply or by human interference, such as pulling the plug. To mitigate the effects that this would have on the system, we plan on including a battery backup subsystem to each microcontroller. The battery backup subsystem will be able to not only power the microcontroller when it is unplugged, but it will also be able to charge the battery when it is plugged in.

Most ESP32 development boards, like the Adafruit HUZZAH32, have this subsystem built in. Should we decide to build this subsystem ourselves, we would use the following parts. Most, if not all, ESP32 microcontrollers use 3.3 volts as its operating voltage, so utilizing a 3.7 volt battery (in either an 18650 or LiPo form factor) with a voltage regulator would supply the necessary voltage for the microcontroller to operate. A battery charging circuit consisting of a charge management controller would also be needed to maintain battery safety and health.

## IoT Core in the Cloud

The IoT Core in the Cloud will handle the main processing of the data sent by the microcontrollers. Each microcontroller is connected to the IoT Core, which will likely be hosted on AWS, through the ESP32's included 2.4GHz Wi-Fi transceiver. We will also host on AWS the web app that interfaces with the IoT Core to display the fullness of the different sectors. This web app will initially be very simple and display only the estimated fullness. The web app will likely be built using a Python web framework such as Flask or Django.

# Criterion For Success

- Identify Wi-Fi and Bluetooth packets from a device and distinguish them from packets sent by different devices.

- Be able to estimate the occupancy of a sector within a reasonable margin of error (15%), as well as being able to compute its fullness relative to that sector's size.

- Display sector capacity information on the web app that is accurate within 5 minutes of a user accessing the page.

- Battery backup system keeps the microcontroller powered for at least 3 hours when the wall outlet is unplugged.

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