Projets novateurs réalisés

L2M – Digital Twin for Real-Time Cardiac Arrest Risk Monitoring

This project has developed a real-time digital monitoring system that identifies people at risk of sudden cardiac arrest by analyzing their heart signals (ECGs). By combining a machine learning model with cloud-based technology, the system automatically updates each patient’s status and sends alerts if a risk is detected. This enables the partner organization to provide faster, more accurate patient monitoring in hospitals or remotely, allowing earlier interventions that could save lives. The system makes heart monitoring more proactive and personalized, improving patient care and safety.

City of Abbotsford Body Worn Cameras Project

As Abbotsford Police Department begins the implementation of the trial phase of their police body-worn camera (BWC) program, a thorough evaluation of the program is needed. The Community Health and Social Innovation (CHASI) Hub will support the Abbotsford Police Department’s work by engaging in the research activities to conduct this evaluation.

L2M – TraffiCore

During the course of this project, the main focus will be on the market research aspect of TraffiCore, a traffic monitoring and control automation startup which aims to provide traffic managers and controllers with a useful tool that can automatically perform the repetitive and mundane tasks that are usually perfomed manually and suboptimally. The intern will conduct interviews with potential customers and users in both the public and the private sectors, validate the problem the startup tackles, validate the developed solution’s desirability, build a business model, and identify risks and competitors. At the end of this program, the solution should have been validated, the understanding of the market enhanced, and the business plan clearly defined.

L2M – Development of a Rapid Molecularly Imprinted Polymer (MIP)-Based Diagnostic Device for Differentiating Bacterial and Viral Infections via Dual Biomarker Detection

We are developing a rapid, low-cost diagnostic device that helps healthcare providers quickly determine whether an infection is bacterial or viral, leading to better treatment decisions and reduced antibiotic misuse. This project will explore real-world demand for the device, evaluate its competitive edge, and refine its market strategy. Through the Lab2Market Validate program, Edmonton Unlimited will support the translation of university-based research into practical healthcare solutions, contributing to the development of a stronger and more effective health innovation landscape in Alberta and beyond.

Using Ergonomic AI software to enhance PDA and Ergonomic Programs

Optimization of an ergonomics program within a manufacturing facility. AI camera based system for postural injury risk identification will be utilized to improve productivity and quality of injury risk identification. The project will then continue to applying the RACE Recognize, Assess, Control, Evaluate model. Physical Demands Analysis and process and design solutions for risk reductions will be proposed. Occupational injuries are a significant burden on Canadian manufacturing companies. Improved identification and ergonomic solutions will improve working conditions for Canadians. .

Optimizing a wireless environmental monitoring system for automated methane quantification in landfills and industrial facilities

Biocovers are earthen structures built into landfills and other industrial facilities to collect and degrade greenhouse gas emissions from the site. They are widely considered one of the most effective ways to combat methane. In order to assess the performance of those biocovers, operators have to attend to the biocover regularly and use a flux chamber and handheld analyzer at different points on the cover and the landfill in order to measure the system’s methane destruction efficiency. The challenge with this method is that it is time-consuming, expensive, and produces very sparse data points.

Spero Analytics is an IoT startup which deploys wireless environmental monitoring systems for the waste management and oil and gas industries. Our aim with this project is to adapt our monitoring system to fit inside an automated, solar-powered flux chamber, thereby converting it into a wireless, autonomous system that can take methane flux measurements every hour with no external, thereby eliminating the need for manual measurement. The research for this project will include designing a robust flux measurement protocol, creating an efficient data transmission and cloud-based analytics system, and ensuring the system is energy efficient.

Composite and foam profiles for building and construction industry: modification of the products and existing extrusion processes

Vision Extrusions Group LTD is a recognized leader in the building products industry. They produce a wide range of products (as shown in Figure 1) including windows, doors, decks, fences, etc. utilizing extruded polymeric profiles as a substitute for wood. In the proposed project, the following objectives have been set by the partner organization to improve the quality and reduce the cost of their products:
(I) Development of a technology that enables the elimination of steel/aluminum stiffeners embedded in extruded PVC profiles (in products such as windows, doors and rails) by increasing the mechanical properties of PVC. (II) Development of a novel approach in order to increase the production rate of extruded PVC foam profiles without sacrificing properties. (III) Production of extruded profiles utilizing recycled thermoplastic/natural fiber composite foams with high processing rates. Improvement of the mechanical properties of such foam profiles through the inclusion of a second reinforcing phase.

Development of a smart software travel assistant for free independent traveler

This project is to build a smart traveler assistant (STA) software system for Free Independent Travelers (FIT). FIT is a travel style where a person plans and books all aspects of their trip themselves, including transportation, accommodations, and itinerary. This STA could plan and book the itinerary before traveling and could replan and rebook the travel activities during traveling according to the real situations. In Dr. Yuhong Yan and her students’ previous research, an automatic travel planner was built based on automatic service composition. All the traveling activities are modelled as services and a service is abstracted as a common model whose properties include cost, time, location, etc. Thus, service composition algorithm is developed to integrate the services into a plan using planning techniques. Although the planning algorithm developed can solve the problem well, the main difficulty is to understand user requirements described in natural language. The current advances in Large Language Model (LLM) can solve the problem of user requirements understanding. We plan to use Retrieval Augmented Generation (RAG) to control how LLM responses to user input. More specifically, the common properties of services are going to be modelled and the values of the properties are going to be extracted using RAG + LLM. Then, we could properly model user requirements and then extend our existing travel planner to build a true STA. Our industrial partner is Concord Tour & Travel. This is a Montreal based travel agency. It has full-fledged product lines from airplane tickets, bus tour, to group travel groups to many destinations. Concord senses the trend changes in tourism industry and is interested to build a smart FIT assistant. Their domain expertise will play a crucial role in ensuring the success of the final product.

Quantum LiDAR Raytracing

Developing quantum-enhanced LiDAR requires a deep understanding of the water medium’s optical properties, which vary with environmental factors. To address this, an oceanic model was created to estimate system performance based on water’s scattering and absorption traits, though it currently simplifies these as identical. In the proposed project, this will be refined. System analysis is equally vital, focusing on beam behavior, optical compatibility, and signal processing using Phantom Photonics’ interferometric approach. A raytracing-based scattering model is being developed to optimize pulse duration and ensure device stability. In parallel, a fiber-based LiDAR system is in development to improve precision, data quality, and SWaP metrics, allowing deployment at depths up to 6000 meters. Interns will support this work, especially by adapting existing free-space optics models for underwater use. These combined efforts aim to boost performance, reliability, and the commercial appeal of Phantom Photonics’ LiDAR systems.

AI for Airplane Engine Inspection

This project aims to develop an AI-powered visual inspection system using image data from P&WC’s existing Computer Vision Inspection System (CVIS) to enhance aircraft engine assembly issue detection. By leveraging deep learning techniques, the proposed solution will address current limitations in accuracy and consistency, targeting a 100% visual detection rate for anomalies such as: part at wrong orientation, missing or extra component, and not-tospec installation. The model will be trained on historical CVIS images, validated against expert annotations, and integrated into a streamlined inspection workflow with human-in-the-loop capabilities for continuous improvement. This initiative will replace a less reliable visual inspection system currently used, reduce time and energy resources expended when out-of-spec parts are shipped by or require off-site repair, and establish a scalable foundation for future smart inspection technologies within aerospace manufacturing and maintenance environments.

L2M – MeshGuard

This project aims to create a communication tool that works without the internet by using mesh networking technology. It is designed to help workers in remote or dangerous areas stay connected, even during emergencies like power outages or network failures. The system can send important information such as location, temperature, and gas levels between devices. The partner organization will benefit by improving worker safety, reducing the risk of accidents, and ensuring better communication in areas with poor internet access.

L2M – Hydrogen Conversion Kits for Heavy-Duty Trucks

Alongside participation in the Lab2Market Validate program, where the intern will test commercial viability of their idea by speaking to potential customers, this Mitacs project consists of a market analysis that identifies ideal vending countries, investigates safety regulations, and estimates market size for the intern’s idea of a hydrogen conversion kit for heavy-duty trucks. The conversion kit would modify internal combustion engines from diesel to hydrogen, thereby eliminating greenhouse gas emissions and reducing fuel costs, as hydrogen derived from waste sources is cheaper than diesel fuel.