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
final_paper1.pdf
proposal1.pdf
video
# 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.

Oxygen Delivery Robot

Aidan Dunican, Nazar Kalyniouk, Rutvik Sayankar

Oxygen Delivery Robot

Featured Project

# Oxygen Delivery Robot

Team Members:

- Rutvik Sayankar (rutviks2)

- Aidan Dunican (dunican2)

- Nazar Kalyniouk (nazark2)

# Problem

Children's interstitial and diffuse lung disease (ChILD) is a collection of diseases or disorders. These diseases cause a thickening of the interstitium (the tissue that extends throughout the lungs) due to scarring, inflammation, or fluid buildup. This eventually affects a patient’s ability to breathe and distribute enough oxygen to the blood.

Numerous children experience the impact of this situation, requiring supplemental oxygen for their daily activities. It hampers the mobility and freedom of young infants, diminishing their growth and confidence. Moreover, parents face an increased burden, not only caring for their child but also having to be directly involved in managing the oxygen tank as their child moves around.

# Solution

Given the absence of relevant solutions in the current market, our project aims to ease the challenges faced by parents and provide the freedom for young children to explore their surroundings. As a proof of concept for an affordable solution, we propose a three-wheeled omnidirectional mobile robot capable of supporting filled oxygen tanks in the size range of M-2 to M-9, weighing 1 - 6kg (2.2 - 13.2 lbs) respectively (when full). Due to time constraints in the class and the objective to demonstrate the feasibility of a low-cost device, we plan to construct a robot at a ~50% scale of the proposed solution. Consequently, our robot will handle simulated weights/tanks with weights ranging from 0.5 - 3 kg (1.1 - 6.6 lbs).

The robot will have a three-wheeled omni-wheel drive train, incorporating two localization subsystems to ensure redundancy and enhance child safety. The first subsystem focuses on the drivetrain and chassis of the robot, while the second subsystem utilizes ultra-wideband (UWB) transceivers for triangulating the child's location relative to the robot in indoor environments. As for the final subsystem, we intend to use a camera connected to a Raspberry Pi and leverage OpenCV to improve directional accuracy in tracking the child.

As part of the design, we intend to create a PCB in the form of a Raspberry Pi hat, facilitating convenient access to information generated by our computer vision system. The PCB will incorporate essential components for motor control, with an STM microcontroller serving as the project's central processing unit. This microcontroller will manage the drivetrain, analyze UWB localization data, and execute corresponding actions based on the information obtained.

# Solution Components

## Subsystem 1: Drivetrain and Chassis

This subsystem encompasses the drive train for the 3 omni-wheel robot, featuring the use of 3 H-Bridges (L298N - each IC has two H-bridges therefore we plan to incorporate all the hardware such that we may switch to a 4 omni-wheel based drive train if need be) and 3 AndyMark 245 RPM 12V Gearmotors equipped with 2 Channel Encoders. The microcontroller will control the H-bridges. The 3 omni-wheel drive system facilitates zero-degree turning, simplifying the robot's design and reducing costs by minimizing the number of wheels. An omni-wheel is characterized by outer rollers that spin freely about axes in the plane of the wheel, enabling sideways sliding while the wheel propels forward or backward without slip. Alongside the drivetrain, the chassis will incorporate 3 HC-SR04 Ultrasonic sensors (or three bumper-style limit switches - like a Roomba), providing a redundant system to detect potential obstacles in the robot's path.

## Subsystem 2: UWB Localization

This subsystem suggests implementing a module based on the DW1000 Ultra-Wideband (UWB) transceiver IC, similar to the technology found in Apple AirTags. We opt for UWB over Bluetooth due to its significantly superior accuracy, attributed to UWB's precise distance-based approach using time-of-flight (ToF) rather than meer signal strength as in Bluetooth.

This project will require three transceiver ICs, with two acting as "anchors" fixed on the robot. The distance to the third transceiver (referred to as the "tag") will always be calculated relative to the anchors. With the transceivers we are currently considering, at full transmit power, they have to be at least 18" apart to report the range. At minimum power, they work when they are at least 10 inches. For the "tag," we plan to create a compact PCB containing the transceiver, a small coin battery, and other essential components to ensure proper transceiver operation. This device can be attached to a child's shirt using Velcro.

## Subsystem 3: Computer Vision

This subsystem involves using the OpenCV library on a Raspberry Pi equipped with a camera. By employing pre-trained models, we aim to enhance the reliability and directional accuracy of tracking a young child. The plan is to perform all camera-related processing on the Raspberry Pi and subsequently translate the information into a directional command for the robot if necessary. Given that most common STM chips feature I2C buses, we plan to communicate between the Raspberry Pi and our microcontroller through this bus.

## Division of Work:

Given that we already have a 3 omni wheel robot, it is a little bit smaller than our 50% scale but it allows us to immediately begin work on UWB localization and computer vision until a new iteration can be made. Simultaneously, we'll reconfigure the drive train to ensure compatibility with the additional systems we plan to implement, and the ability to move the desired weight. To streamline the process, we'll allocate specific tasks to individual group members – one focusing on UWB, another on Computer Vision, and the third on the drivetrain. This division of work will allow parallel progress on the different aspects of the project.

# Criterion For Success

Omni-wheel drivetrain that can drive in a specified direction.

Close-range object detection system working (can detect objects inside the path of travel).

UWB Localization down to an accuracy of < 1m.

## Current considerations

We are currently in discussion with Greg at the machine shop about switching to a four-wheeled omni-wheel drivetrain due to the increased weight capacity and integrity of the chassis. To address the safety concerns of this particular project, we are planning to implement the following safety measures:

- Limit robot max speed to <5 MPH

- Using Empty Tanks/ simulated weights. At NO point ever will we be working with compressed oxygen. Our goal is just to prove that we can build a robot that can follow a small human.

- We are planning to work extensively to design the base of the robot to be bottom-heavy & wide to prevent the tipping hazard.