From improving access to healthcare diagnostics to advancing human–computer interaction and reducing administrative burden for clinicians, this year’s Department of Electrical and Computer Engineering (ECE) capstone projects demonstrate how thoughtful engineering can address meaningful real-world challenges. Working in teams, ECE students applied technical expertise in artificial intelligence, embedded systems, sensing technologies and software design to develop practical solutions that improve quality of life, increase efficiency and expand access to critical services.
The capstone experience represents the culmination of students’ undergraduate education, integrating knowledge gained through coursework, research and Waterloo’s co-op program. The projects highlight ECE’s strengths in combining strong technical foundations with human-centred design, enabling students to develop innovative technologies that respond to evolving needs in healthcare, industry and everyday life.
µAI
Sensor fusion technology removes guesswork from microwave cooking
Traditional microwave ovens rely on manual timers, often leaving food unevenly heated — too cold in some areas and overheated in others. To address this everyday frustration, Azizul Chowdhury, Fasih Noor, Ahsen Qureshi, Yash Sethi, Jason Sidhu and Faizan Syed developed µAI, an intelligent heating system that automatically determines optimal heating time and temperature. Rather than relying on user estimation, the system applies sensor fusion and machine learning to identify food type and monitor temperature in real time.
µAI integrates an infrared thermal camera with an object detection model trained to recognize common food items. By continuously analyzing thermal feedback, the system adjusts heating dynamically to ensure more consistent results. Unlike many existing “smart” appliances that emphasize connectivity features, µAI focuses on improving core performance, allowing the microwave to better understand what is being heated and how it should be heated.
Designed as an intelligent feedback system, µAI demonstrates how AI-driven automation can enhance everyday household technologies. By removing guesswork and improving reliability, the project highlights how embedded intelligence can improve convenience, reduce food waste and create more intuitive kitchen experiences.
µAI team: Azizul Chowdhury, Fasih Noor, Ahsen Qureshi, Yash Sethi, Jason Sidhu and Faizan Syed
TherAIpy Assistant team: Sairah Amuthan, Alexander Cholmsky, Eric Gan and Rick Pan
TherAIpy Assistant
AI-powered platform automates psychological assessment workflows, helping clinicians save time and focus on student care
As student mental-health needs continue to rise, school psychologists are facing increasing caseloads and ongoing staffing shortages. Yet much of their time is still consumed by repetitive administrative work such as scoring paper assessments, interpreting statistical results and drafting detailed reports for parents and educators. Inspired by firsthand insight into the time-intensive nature of psychoeducational evaluations, Sairah Amuthan, Alexander Cholmsky, Eric Gan and Rick Pan developed TherAIpy Assistant, an intelligent system designed to streamline the assessment workflow and allow clinicians to focus more on supporting student success.
The platform uses optical character recognition (OCR) to digitize clinician forms, automatically extract scoring data and generate key statistical insights. By combining structured assessment results with contextual information, the system leverages generative AI to produce an initial draft of a psychological report that clinicians can refine. Through close collaboration with practicing psychologists, the team iterated on the system to ensure strong report quality while maintaining careful handling of sensitive personal health information. The result is an efficient and affordable solution — costing approximately six cents per report — that can save clinicians two or more hours of manual work per assessment.
Designed with scalability in mind, TherAIpy Assistant aims to support a wide range of clinical workflows beyond its initial use case. Future development includes integrating additional behavioural reports and expanding customization options to meet the needs of different psychological practices. By reducing administrative burden and improving workflow efficiency, the system enables psychologists to dedicate more time to interpretation, care and meaningful student outcomes.
SkyStation
Wearable input device promotes healthier, more natural human–computer interaction
Prolonged computer use can contribute to wrist strain, repetitive motion injuries and poor posture, yet traditional mouse-and-keyboard setups often limit opportunities for movement. Seeking to improve long-term ergonomics, Julia Aziz, Andrew Bovenkamp, Naser Nassar, Max Storjohann, Reid Schwartzentruber and Anthony Zhelnakov developed SkyStation, a wearable input device designed to support more natural and flexible interaction with computers.
The system uses a lightweight glove equipped with inertial measurement sensors and capacitive touch sensors to translate intuitive hand movements into precise mouse control. Through sensor fusion and wireless communication, users can interact with digital interfaces fluidly without being confined to a desk. Unlike camera-based gesture systems, the device provides consistent responsiveness while allowing users to move freely during presentations, reading or everyday computing tasks.
Developing SkyStation required integration of custom PCB design, embedded systems programming and wireless hardware optimization. The resulting device includes long battery life and haptic feedback to confirm user input in real time. By combining human-centred design with advanced engineering, the project demonstrates how technology can support both productivity and physical wellbeing.
SkyStation team: Julia Aziz, Andrew Bovenkamp, Naser Nassar, Max Storjohann, Reid Schwartzentruber and Anthony Zhelnakov
PinPoint team: Matthew Garth, Jack Greenwood, Neil Kaus, Sebastien Paradis and Easson Weisshaar
PinPoint
Automated PCB probing platform streamlines hardware testing and debugging
Testing printed circuit boards (PCBs) and debugging firmware often requires engineers to manually probe extremely small components — a time-consuming and error-prone process that can slow development and limit productivity. Matthew Garth, Jack Greenwood, Neil Kaus, Sebastien Paradis and Easson Weisshaar developed PinPoint, an automated PCB-probing system that transforms traditional hardware validation into a more efficient, intelligent workflow.
By parsing PCB design files and translating them into precise probe paths, PinPoint allows engineers to test circuit boards through an intuitive web-controlled interface. The AI-integrated system enables users to capture electrical signals, monitor performance and troubleshoot issues with high precision, while reducing setup time and minimizing the risk of human error. Remote accessibility further expands flexibility, allowing engineers to interact with hardware systems without being physically tied to laboratory environments.
Designed as a compact benchtop solution, PinPoint makes advanced testing capabilities more accessible beyond large-scale manufacturing settings. By bringing automation to a traditionally manual process, the project helps accelerate iteration cycles and enables engineers to focus more on innovation and problem-solving rather than repetitive diagnostic work.
FeetBack
Smart insoles bring continuous monitoring to improve access to podiatric care
More than eight million Canadians experience foot-related health concerns each year, yet access to timely podiatric care remains limited. Diagnoses are often based on brief clinical observations that capture only a snapshot of a patient’s condition. Steven Armstrong, Lucas Delvoye, Jalen Liew, Staisha Neville and Tristan Parry developed FeetBack, a Bluetooth-enabled smart insole designed to provide continuous insight into foot health by monitoring plantar pressure distribution, gait alignment and temperature over extended periods of everyday use.
The system combines embedded sensing technology with wireless communication and mobile application development to simulate the functionality of in-clinic pressure platforms in a portable form factor. A network of integrated sensors captures real-time biomechanical data, which is processed through an onboard system and transmitted to a companion mobile app. Throughout development, the team addressed challenges related to miniaturization, power management and reliable Bluetooth connectivity, optimizing PCB design to ensure all components fit within the slim insole profile.
By shifting foot health monitoring from occasional clinic visits to continuous daily tracking, FeetBack enables clinicians to observe patterns that may otherwise go undetected. With growing interest from healthcare professionals, the team sees strong potential for the technology to evolve into a practical tool that supports earlier intervention and improves long-term outcomes in podiatric care.
FeetBack team: Steven Armstrong, Lucas Delvoye, Jalen Liew, Staisha Neville and Tristan Parry
Together, these five projects demonstrate the breadth of innovation emerging from the Department of Electrical and Computer Engineering, spanning artificial intelligence, embedded systems, sensing technologies and human-centred design. By applying engineering principles to real-world challenges, ECE students are developing solutions that improve healthcare delivery, enhance everyday interactions with technology and increase efficiency across industries. The capstone program reflects the department’s commitment to experiential learning and its focus on preparing graduates to contribute meaningful technological advancements in Canada and beyond.