Past Capstone Projects

Optimizing Safety & Efficiency: Advancements in a Naloxone Auto-Injection Device
Team members: Amy, Hannah, Irene, Sara, Syvanna
Supervisors: Dr Ned Djilali, Dr Christopher Dennison, Art Makosinski

The project aimed to improve the safety, reliability, and efficiency of an auto-injection device developed by a research group at the 番茄社区. The device is designed to be worn on the upper arm and functions by continuously monitoring one’s SpO2 level which is an initial indicator of an overdose. When SpO2 levels drop rapidly compared to baseline levels, the device automatically injects the user with a full dose of Naloxone, reversing the overdose and allowing the user to seek further emergency aid. The team made significant progress, particularly in the improvement of injecting efficiency, by achieving a detection, needle insertion, injection, and needle withdrawal time of ~6 seconds. This is within the incredibly short time range needed for the device to be useful in case of an overdose. Although the design needs further improvements before the device is ready for clinical trials, the team was honoured to be part of a hugely meaningful project with the potential to save countless lives.

The project won the first place IEEE Kelly Manning Award in July 2023.

Mouthguard Impact Testing
Team members: Sabrina, Kieran, Kayla, Rudra Aryan and Serena
Supervisor: Dr Christopher Dennison

At the request of World Rugby and in the absence of a current standardized testing system, the team designed a 3D-printed human jaw model that can be used to test the effectiveness of sports mouthguards. The impact testing method consisted of a drop tower that dropped a mass onto a model with an integrated piezoresistive sensor on the teeth. The sensor measured voltage changes and then converted it to a force measurement which was then compared to impacts with and without mouthguards.

Innovative Wearable Foot Sensor for Gait Data Collection in Real-World Settings
Team members: Elana, Adriel, Sophie and Coy
Supervisor: Dr Marianne Black

Gait analysis plays a crucial role in assessing patient status in various diseases, such as osteoarthritis, multiple sclerosis, and Parkinson's. Stride parameter data allows us to monitor and predict disease progression, aiding treatment planning. Currently, gait assessments include a degree of subjectivity and occur in controlled environments over a short period of time.

Focusing on data collection for research applications, we developed a wearable foot sensor that measures various stride parameters. The sensor connects via Bluetooth to our mobile app, WalkWell. By collecting real-world data over extended periods of time, we can quantify trends in disease progression and create data-informed models to predict progression types early enough for corrective measures to be taken, and keep individuals 'walking well'.

Future work could include a more user-centered approach, where our app could intelligently alert users to gait abnormalities, provide personalized recommendations to improve gait, and empower users to take proactive measures for enhancing gait and overall well-being.