Dalrymple, O. O. (2024, April), Enhancing Mobility for the Visually Impaired: A Community-Centered Capstone Project  Paper presented at 2024 ASEE PSW Conference, Las Vegas, Nevada. 10.18260/1-2--46037

\bibitem{asee_peer_46037}
  Dalrymple, O. O. (2024, April), \emph{Enhancing Mobility for the Visually Impaired: A Community-Centered Capstone Project}
  Paper presented at 2024 ASEE PSW Conference, Las Vegas, Nevada.
  10.18260/1-2--46037

Odesma Onika Dalrymple.  "Enhancing Mobility for the Visually Impaired: A Community-Centered Capstone Project".  2024 ASEE PSW Conference, Las Vegas, Nevada, 2024, April.  ASEE Conferences, 2024.  https://strategy.asee.org/46037 Internet.  30 Jun, 2024

\bibitem{asee_peer_46037}
  Odesma Onika Dalrymple.
  "Enhancing Mobility for the Visually Impaired: A Community-Centered Capstone Project".
  \emph{2024 ASEE PSW Conference, Las Vegas, Nevada, 2024, April}.
  ASEE Conferences, 2024.
  https://strategy.asee.org/46037 Internet.  30 Jun, 2024

@INPROCEEDINGS{asee_peer_46037
author = "Odesma Onika Dalrymple"
title = "Enhancing Mobility for the Visually Impaired: A Community-Centered Capstone Project"
booktitle = "2024 ASEE PSW Conference"
year = "2024"
month = "April"
address = "Las Vegas, Nevada"
publisher = "ASEE Conferences"
note = {https://strategy.asee.org/46037}
number = {10.18260/1-2--46037}
}

TY  - CPAPER
AB  - Abstract-Reviewed Presentation: As part of the Engineering Exchange for Social Justice (ExSJ), our team of 10 senior computer science students is actively immersed in a community-centered capstone project dedicated to enhancing mobility for visually impaired individuals. Guided by our community partner, we are utilizing the capabilities of iPhones 12 and above, specifically their Lidar technology and processing power, to develop a chest-mounted wearable app. This app strategically employs the YOLOv8 framework for swift object identification, prioritizing speed over type-of-object accuracy.
Our Computer Vision model consists of two key components: object detection/classification and threat detection. By analyzing the growing rate of bounding box tags, our model identifies potential threats, calculating if an object is approaching the user and providing crucial real-time information for adaptive navigation. Despite sacrificing type-of-object accuracy to prioritize real-time speed, our strategic decision underscores the urgency of delivering instantaneous and reliable environmental feedback to users. We plan to refine our solution through extensive model training and testing in Unity, simulating diverse environments and assessing performance metrics.
Addressing critical gaps in assistive technologies, our solution aims to improve tracking accuracy for steps, upsteps, curbs, and poles. By incorporating these challenges into our Computer Vision model, our technology strives to excel not only in rapid object detection but also in providing a comprehensive and reliable aid for navigating diverse urban environments.
In our proposed presentation, we aspire to share experiences, insights, and breakthroughs, contributing to the discourse on inclusive technology solutions for the visually impaired community.

AU  - Odesma Onika Dalrymple
CY  - Las Vegas, Nevada
DA  - 2024/04/18
PB  - ASEE Conferences
TI  - Enhancing Mobility for the Visually Impaired: A Community-Centered Capstone Project
UR  - https://strategy.asee.org/46037
DO  - 10.18260/1-2--46037
ER  -