Innovative Projects Realized

L2M – Robotic Venipuncture System for Automated Blood Collection

Venipuncture is a widely performed yet error-prone medical procedure, posing risks to both patients and healthcare workers due to reliance on visual inspection and palpation for vein access. High rates of needlestick injuries, repeated insertion attempts, and challenges with difficult patient populations highlight the need for safer, more reliable solutions, especially amid workforce shortages and growing clinical pressures. This project proposes a robotic venipuncture system integrating AI-guided imaging and robotic assistance to enhance first-attempt success rates and reduce caregiver burden. However, given the complexity of healthcare markets, characterized by regulatory constraints, multiple stakeholders, and long adoption cycles, its commercial viability must be rigorously validated. Through structured market research, stakeholder engagement, and competitive analysis, the project aims to determine product-market fit, identify early adopters, explore pricing models, and ensure the solution addresses a prioritized clinical need rather than a perceived one. This project will produce a clear, evidence-based commercialization roadmap that reduces investment risk, guides product positioning, and ensures alignment with real clinical and operational needs. By grounding the technology in validated market insights, it will enable the partner organization to move from promising technical prototypes to strategically de-risked business opportunities with defined market entry strategies. The outcomes of this work will inform pricing, positioning, and pilot deployment plans in healthcare settings, laying the foundation for a viable path to commercialization.

L2M-User-preference based indoor path planning

While outdoor navigation systems like Google Maps have become highly advanced, finding efficient paths indoors such as inside hospitals, airports, or shopping malls remains a major challenge. Most existing indoor navigation tools are static, meaning they cannot adapt to real-time changes such as crowded areas or accessibility barriers. They also rarely account for the diverse needs of users, including those with mobility limitations.
This project aims to solve these challenges by developing a computer vision-based, congestion-aware indoor pathfinding system. Using camera feeds and advanced algorithms, the system will predict crowd density in real time and generate optimized routes that adapt dynamically as conditions change.
The innovation of this project lies in three main aspects:
– Real-time Congestion Prediction: Instead of only finding the shortest path, the system uses computer vision to detect and forecast crowded areas, automatically adjusting routes to save time and reduce frustration.
– Personalized Accessibility Integration: The system tailors routes to user preferences such as avoiding stairs, construction zones, or tight spaces ensuring safe and inclusive navigation for all, especially for individuals with mobility challenges.
– System-level Optimization: Beyond individual users, the model distributes foot traffic intelligently across multiple routes, reducing bottlenecks and improving overall flow in large indoor spaces.
In practical use, this technology will reduce travel time, improve comfort and accessibility, and enhance the efficiency of crowded environments like hospitals, transit hubs, and malls. By combining real-time adaptability with user-centered inclusivity, the project delivers a next-generation indoor navigation solution that addresses current gaps and contributes to smarter, more accessible public spaces.

L2M – SmartMed: AI-Powered Platform for Alzheimer’s Medication Management

Medication non-adherence is a major challenge in Alzheimer’s care. Fewer than half of patients take their medications as prescribed, leading to faster disease progression, preventable hospitalizations, and significant emotional and financial strain for families and caregivers. Traditional pillboxes and reminder apps have not solved this issue — they are not designed for people living with cognitive decline or managing complex medication schedules. Once patients leave the pharmacy, pharmacists and caregivers often lose visibility, leaving a critical gap in ongoing support.
This project develops a smart medication management system that combines intelligent hardware and AI-powered software to close this gap. The system features a smart dispensing device with an integrated camera and speaker to ensure the right dose is taken at the right time. Connected IoT sensors and agentic AI algorithms monitor adherence in real time, detect missed doses or irregular behaviors, and generate early alerts to caregivers and pharmacists.
Through interactive dashboards, families, pharmacists, and healthcare providers can view adherence trends, receive predictive alerts, and take timely action before health issues escalate.
This innovation goes far beyond a simple pillbox — it creates a smart, compassionate support system that helps patients stay independent and safe, empowers caregivers with real-time insights, and strengthens the connection between pharmacies and healthcare teams. By combining hardware innovation, IoT connectivity, and AI-driven analytics, the project advances Canada’s leadership in digital health, aging-in-place solutions, and smart pharmacy technology.

L2M – Portable Ultrasound AI for On-Farm Beef Carcass Grading

We’re developing a user-friendly, portable ultrasound system with built-in artificial intelligence to measure important beef carcass qualities like marbling, backfat, and ribeye area directly on the farm, in real time.

This system includes a handheld scanner that connects to a small device. The device instantly analyzes the ultrasound images and shows straightforward results on a screen. This setup means that farm staff can get the information they need without needing a specialist to interpret the scans.

This technology helps both producers and our partner organization make quicker, smarter choices concerning feeding strategies, marketing approaches, and genetic selections well before the animals are sent for processing. We expect this system to bring several advantages: reduced costs for scanning, less interruption to daily farm activities, and more consistent, unbiased results across different locations and users.

By bringing accurate grading into the earlier stages of beef production, this project aims to increase profitability, cut down on waste, and promote herd management practices driven by data for our partner’s network of feedlots and processors. It will allow them to identify superior animals earlier, adjust feeding to improve marbling, and make better breeding decisions. This results in higher quality beef, less waste, and increased profitability. The instant analysis and automated reporting the system brings removes much of the subjectivity from carcass grading, ensuring consistent standards and facilitating fair transactions between producers and processors. The system allows for real-time data sharing. This promotes greater collaboration and transparency across the entire beef production chain.

Drying Fuel Alcohols and Natural Gas with Biosorbents Based on Agricultural By-products

Saskatchewan is one of the key provinces in Canada to produce canola, flax, barley, wheat, and other agricultural products. In this research work, high performance bio-adsorbents will be formulated from the agricultural by-products for drying bio-alcohols and natural gas at low costs. These biosorbents are re-usable for dehydration and regeneration cycles. The exhausted biomaterials may be used for fuel bio-alcohols production through gasification or fermentation. The technology generated from this research will make it possible to dehydrate water containing bio-alcohols and natural gas to achieve fuels of high purity. Use of the biosorbents for this purpose will not require any new facility other than that in the molecular sieves process for drying bio-alcohols or natural gas in industry. In addition, in this research will the biosorbents be characterized. Investigation of adsorption kinetics and equilibrium will contribute to the knowledge of adsorption using biomaterials. Results obtained from this project are expected to provide important information on improving the performance of dehydration of bio-alcohols and natural gas in the current industry. The implementation of this technology will benefit partner industries as well as Sasktchewan agrucultural industries economically.

Automating the Identification of Cold Water Refugia in Salmon Bearing Streams

Using geographic information systems (GIS) software and existing radiometric thermal orthomosaics, automate the process of identifiying and locating areas of cold water refugia in salmon-bearing streams and rivers in BC.

Assessing Social Cognition in Autistic Youths and Adults

The goal of the project is to develop a series of standardized, performance-based computerized assessment tools for use in the examination of social cognition deficits in ASD. Drawing on the available experimental literature a number of potential assessment tasks will be identified, reviewed and configured for computer-based administration to children / youth with ASD. Programming of assessments will be followed by pilot testing, evaluation and validation in ASD and control children/youth. Key findings from analyses will be used to formulate recommendations regarding subtest modifications, inclusion and additional measures

A systems level optimization of the solar battery in a solar energy system

SunVault Energy Incorporated, a Kelowna, British Columbia based energy technology company, aims to become a significant player within the emerging Canadian solar industry. They have devised a technology, the solar battery, which integrates the energy storage and energy generation capabilities of a stand-alone solar energy system. They view this as potentially a means of lowering costs, increasing efficiencies, and reducing losses. In this project, the intern will explore the performance of the stand-alone solar energy systems that include the solar battery and contrast the obtained result with the conventional solar energy system. Means of improving the overall system performance will be sought. Finally, if time permits, pathways to commercialization, with a focus on technological improvements to the device and the overall system, will be sought.

Testing gravity using data from the Euclid satellite mission

One of the biggest mysteries in modern physics is “dark energy”. This strange form of energy makes up over half of our Universe and is causing space to expand faster and faster. Even 30 years after its Nobel Prize-winning discovery, we still have no clear idea what dark energy actually is. Scientists have come up with many different theories to explain dark energy in the last decades. Additionally, new telescopes have been constructed to explore larger parts of the Universe than ever before. These telescopes are now starting to gather huge amounts of data about millions of galaxies, giving us a unique opportunity to test new dark-energy theories. This process requires novel data analysis methods based on powerful machine learning tools that we will develop in this project.  The internship combines the skills from theory specialists of the University of Edinburgh with the data-analysis experts from the University of Waterloo.
These newly developed methods will be used in two of the world’s biggest upcoming galaxy surveys: the European-led Euclid satellite mission and the North American DESI instrument. This internship brings together teams from both sides of the Atlantic to solve one of the largest puzzles in our Universe.

Township of Ramara Economic Development and Community Improvement Plans

The Township of Ramara is updating its Economic Development Strategy and Community Improvement Plan to better reflect current community and business needs. This project will support that process by collecting and analyzing data, researching best practices from other municipalities, and engaging residents and local businesses through surveys and interviews. The results will help the Township create a clear, evidence-based plan that supports local growth, attracts investment, and strengthens community participation. The work will provide Ramara with practical recommendations to guide decision making and promote long-term economic and social sustainability.

Étudier l’effet de la pression et de la température sur la valeur nutritive d’un jus de betterave destinés aux sportifs

Un changement dans les mentalités s’opère graduellement qui tend à favoriser la consommation d’aliments les moins transformés possibles, voire les plus naturels. Ce projet de recherche propose une nouvelle approche pour la fabrication d’un jus de betterave en appliquant les hautes pressions. Le jus de betterave dont la consommation est recommandée, tant pour ses qualités nutritionnelles que pour ses propriétés fonctionnelles. Par exemple, il est reconnu que la betterave renferme de la bétamine et d’anthocyanines. De plus, la betterave est connue comme l’un des rares végétaux qui contiennent des bétalaïnes. Toutes ces molécules ont des propriétés antioxydantes par excellence. Des travaux d’optimisation seront réalisés dans le but de fixer les paramètres tels que la pression, le temps, la température et le pH pour garder les éléments nutritifs du jus. Cette étape sera réalisée chez l’entreprise Végétamix Inc et visera, en premier lieu à étudier l’effet de chaque paramètre sur la qualité du jus puis en deuxième lieu, l’effet combiné de ces quatre paramètres sur la qualité du jus désirée. Des analyses comparatives seront réalisées à l’université Laval.

Management and mining of big energy sector data

With advances in techniques, high volumes of valuable data are generated in many domains (e.g., energy sector) at a rapid rate. Consequently, a scalable and flexible system for efficient storage and fast management of these distributed data is needed. In this proposed research project, we plan to design and implement a cloud-based data storage & management system that is flexible, scalable and fast to handle distributed data in a parallel fashion for the partner organization. In addition, we also plan to design and implement a system that conducts parallelized data mining and machine learning to discover new patterns from the data stored in the above data management system. These discovered patterns would be beneficial to the partner organization as these patterns help complex decision making.