Projets novateurs réalisés

L2M_Marketing Strategy for VT-Patch, a medical smart patch as RPM system.

The VT-PATCH project addresses a crucial gap in pediatric healthcare by introducing a user-friendly medical patch
designed for short-term, continuous monitoring of infants and young children. Unlike the current complex system of
multiple devices and wires, the VT-PATCH offers an all-in-one solution to measure vital signs like heart rate,
respiratory rate, blood oxygen saturation, and body temperature. This innovative approach not only improves the
quality of care for sick children but also enhances operational efficiency in healthcare facilities. By facilitating earlier
patient release, the project increases bed availability, reduces nursing requirements, saves costs, minimizes energy
consumption, and optimizes time management. The VT-PATCH stands out in the market as a tailored solution for
pediatric patients, ensuring effective remote monitoring from an early age and contributing to the advancement of
pediatric healthcare in Canada.

European Responses to China’s Support for Russia in its War against Ukraine: A Study of the Czech Republic, Poland, and the Baltic States

This research project is situated within the broader scientific context of international relations, security studies, and European foreign policy. In recent years, the geopolitical landscape has been significantly shaped by China’s increasing global influence and its complex relationships with both Russia and the West, and it changed even more drastically when the Russia has started its full-scale invasion to Ukraine on February 24, 2022. The study would fill a gap in the literature on Europe-China relations in the context of the ongoing war in Ukraine. While most existing analyses focus on the EU’s collective stance or major Western European powers like Germany and France, this project highlights the often-overlooked perspectives of the Central-Eastern Europe (CEE) countries that are among the most vocal critics of Russian aggression. Relatively little or no scholarship has focused on how specific CEE countries, particularly the Czech Republic, Poland, and the Baltic states, perceive and react to China’s indirect support for Russia. By examining potential variations in the countries’ responses—particularly in light of their differing levels of support for Ukraine—the research aims to identify patterns and explanations behind these differences.

An investigation into the failure trends of an AI tool for the detection of tumour margins

Breast cancer is a global health challenge, affecting millions of women annually. In 2020, it was the most common cancer in 109 countries, including Canada. Breast-conserving surgery (BCS), or lumpectomy, is the standard of care for early-stage breast cancer. The goal of BCS is to remove malignant tissue while preserving healthy tissue but achieving tumor-free margins remains a significant challenge. Permanent histopathology, the gold standard for margin assessment, typically takes 2–5 days, leading to reoperations in over 20% of cases due to positive margins.

Perimeter Medical Imaging AI, a Toronto-based medical device company founded in 2013, has developed an OCT device with an embedded AI decision support module. This tool provides real-time feedback on tumor margins during surgery, potentially reducing reoperation rates. However, the AI module’s performance is not uniform across all patient subgroups and device conditions. A retrospective efficacy study of the AI tool has been published in a peer-reviewed journal, but further investigation is needed to understand its limitations and improve its reliability.

This project will focus on identifying trends in the AI tool’s failure modes, including false positives and false negatives, across different demographics, disease types, and device variability. By addressing these limitations with changes to the annotation or training data composition, we aim to enhance the tool’s accuracy and usability, ultimately improving patient outcomes.

Réingénierie d’un processus de classification de la qualité de placages de bois

Ce projet de recherche consiste à classifier automatiquement du bois plaqué selon 16 catégories à l’aide d’apprentissage automatique. Il sera réalisé en collaboration avec une entreprise, R.Perron, qui collaborera activement pour ce projet en fournissant des données réelles de production ainsi que l’avis d’expert dans ce domaine. Actuellement, cette classification est faite visuellement par un opérateur. Étant donné le grand nombre de catégories (16), l’entreprise souhaite automatiser ce processus, car celui-ci est laborieux et difficile pour l’opérateur. De plus, des erreurs de classification peuvent causer un retour des produits de l’entreprise par ses clients, ce qui emmène des pertes. Aucune donnée n’est actuellement disponible, donc l’objectif de ce projet sera à la fois d’entraîner des modèles de vision pour effectuer cette classification ainsi que d’effectuer la collecte des données, le tout dans un contexte réel de production. Ce projet a donc une nature exploratoire, car nous serons emmenés à tester différentes caméras ainsi que différents positionnements de celle-ci.

Hazelnut alley cropping for the Pacific Northwest

Commercial hazelnuts are a woody perennial crop that requires significant upfront investment. They are typically grown in a monocropped orchard, with bare soil or a closely-cropped grassy orchard floor. Hazelnuts are usually harvested from the ground, meaning that the presence of intercrops is a hindrance for harvest. This results in lower in-field diversity, wasted production space, and unrealized income potential. The use of alley space for production has the potential to increase food and nutrient yields as well as economic returns from the system, but management strategies for doing so without interfering with nut harvest have yet to be established for the Pacific Northwest.

We will establish a hazelnut alley-crop system with the goal of developing strategies for allowing both nut and intercrop harvest. We plan to evaluate the feasibility of intercropping with hazelnuts under three different canopies – single stem, multi-stem, and hedging forms. Strategies may include using harvest technologies such as straddle-harvesters and ATV-pulled harvesters that work in narrow spaces. We will also experiment with different intercrops to develop best practices with the goals of with the goals of allowing efficient nut harvest, maximizing food production from the intercrop, and maintaining soil health and fertility under organic management.

Development of new solid lubricant coatings for different industrial applications

This project focuses on the development, testing, and validation of next-generation solid lubricant coatings for cutting tools designed for hard-to-cut materials commonly used in aerospace and automotive industries. Traditional machining processes often rely on oil- and water-based lubricants, leading to excessive waste, environmental contamination, and increased operational costs. By introducing advanced solid lubricant coatings, this project aims to enhance machining efficiency while reducing industrial waste and water consumption.
The research is centered on improving tool performance in demanding machining applications. The coatings are engineered to minimize friction, reduce heat generation, and extend tool life, ultimately optimizing productivity. Hard-to-cut materials, such as heat-resistant alloys and composites, pose significant challenges in conventional machining. These coatings provide a dry, eco-friendly solution that improves cutting efficiency and also decreases reliance on liquid lubricants.
By addressing critical challenges in machining technology, this initiative supports a transition toward greener, more sustainable manufacturing practices. The outcomes have the potential to benefit industries seeking high-performance solutions that align with environmental sustainability goals while maintaining production efficiency.

Implementing a Computer Vision-based Collision Avoidance System for a Collaborative Surgical Assistant Robot

This project aims to enhance the safety, precision, and efficiency of robotic surgical assistants — collaborative robots designed to work alongside surgeons — by developing a computer vision-based system that prevents collisions in real time. Using cameras and advanced image processing techniques, the system will monitor the operating room, detecting people and other obstacles. It will then use an adaptive decision-making to adjust the movements of the robot based on sensory information. By reducing risks for both patients and medical staff, this technology will make robotic assistance more reliable, reducing the physical strain on surgeons. In the long term, this will allow surgical teams to operate more efficiently, reducing the number of personnel needed per procedure, and thus improving accessibility to advanced surgeries in underserved regions. Participating institutions benefit by advancing their research in artificial intelligence, robotics, and healthcare technology while fostering interdisciplinary collaboration. This project will also contribute to the development of new safety policies for collaborative robots, bridging the gap between computer vision research and surgical robot safety, and paving the way for more intelligent, adaptive, and safer robotic assistants.

Enhancing light-matter interactions with intercalated transition metal dichalcogenides

This research project pioneers the development of novel molybdenum disulfide (MoS2)/copper hybrid materials through electrochemical intercalation and exfoliation techniques. By transforming readily available powdered molybdenite—a byproduct of Canadian mining operations—into atomically thin layers with enhanced properties, the work creates a sustainable pathway for producing high-performance two-dimensional materials without organic additives that typically compromise conductivity. The integration of copper introduces unique plasmonic resonances that dramatically enhance light-matter interactions in the near-infrared spectrum, opening the possibility for advanced photodetectors with exceptional responsivity. This collaboration benefits the German host institution by advancing cutting-edge nanomaterial fabrication methods and quantum material design, while providing the Canadian home institution with an innovative approach to add value to domestic natural resources. The project’s focus on scalable production methods bridges fundamental quantum materials science with practical industrial applications, potentially advancing fields ranging from quantum sensing to night-vision technologies, while strengthening both countries’ positions in sustainable advanced materials development.

L2M – Commercial Validation and MVP Development of a Pediatric Biofeedback Tool for Grip and Pinch Strength Assessment

Pediatric therapists struggle to obtain accurate grip strength measurements because current tools were originally designed for adults. These tools require excessive hand strength and fail to engage children, leading to unreliable data. As a result, therapists often resort to subjective estimates, making it difficult to track progress, tailor treatment plans, or justify therapy to insurance providers. Our solution is a child-friendly grip strength tool with sensory feedback (lights and sounds) to make assessments engaging and ensure precise, reliable data.

Through the L2M Internship, we will focus on three key areas: IP & regulatory groundwork, business strategy, and product development. First, we will file a provisional patent and prepare regulatory pre-submissions to navigate compliance for our Class II medical device. Second, we will refine our go-to-market strategy by assessing distribution channels, reimbursement pathways, and commercialization partners. Third, we will test and iterate our prototype with pediatric therapists and children, gathering feedback to refine our design and ensure usability. Our goal is to finalize our Minimum Viable Product (MVP) by Q3 2025.

By the end of the internship, we will be positioned for early 2026 fundraising to support batch manufacturing and market launch, assuming we meet regulatory and IP milestones. This project will lay the foundation for bringing an innovative, evidence-based grip strength assessment tool to market, ensuring pediatric therapists have the data they need to make informed treatment decisions while making therapy more enjoyable for children.

Life Hacks and Gadgets project

Over six million people living in Canada have arthritis, making it one of the most common chronic conditions. The condition significantly impacts activities of daily living, workplace participation, and it is the leading cause of disability. Living with arthritis leads to pain and physical impairments and people with arthritis can self-identify as living with a disability. When impairments are combined with the lack of accessibility, people with arthritis must adapt activities of daily living and other tasks. Over 70% of persons with disabilities reported that they had experienced 1 or more of the 27 types of barriers to accessibility. Often, assistive devices (ADs) are used as well as simple and practical tips (“life hacks”) to address these limitations and barriers. To address these challenges, the Canadian Arthritis Patient Alliance (CAPA) has worked with five Occupational Therapy students from McMaster University’s Department of Rehabilitation since 2023. These students have conducted a literature identify evidence about assistive devices and arthritis, conducted a survey about the use of assistive devices in daily life, completed interviews with people with arthritis where they shared how they live life well with arthritis and a Photovoice project to bring community knowledge further to the forefront by identifying innovative adaptive strategies or devices, personal accommodations, and coping mechanisms that help people with arthritis live independently. The Mitacs funding will enable us to synthesize all of the available evidence in a way that is useful for patient decision making and design an appropriate website or other layout to communicate the evidence to the patient community.

System level defenses for Enterprise AI Agents

(1) the main activities of the partner
ServiceNow develops a platform for client organizations to manage and automate large-scale processes across various industries. In 2020, ServiceNow acquired Element AI to strengthen its presence in the Artificial Intelligence (AI) research landscape and the Canadian AI ecosystem. This acquisition enabled the development of AI-driven products that improve the platform’s capabilities. ServiceNow Research has made significant contributions to the field of foundation models, notably in Natural Language Processing (NLP), and has a strong presence in developing generative models for different data domains.
(2) the challenges the partner aims to solve through this project
The project seeks to address the security and safety challenges posed by autonomous AI agents in enterprise settings. Specifically, it aims to mitigate risks such as data exfiltration, prompt injections, privilege escalation by developing system-level defenses that make AI agents secure against adversarial attacks. The goal is to maintain the utility and autonomy of AI agents while safeguarding against security threats from AI agents operating in untrusted environments.
(3) the anticipated social or economic benefits of the project for the partner organization(s)
The project is expected to enhance the security of autonomous AI agents, which will safeguard sensitive data and protect against malicious attacks. This will not only boost productivity and efficiency in enterprise settings but also foster a safer digital environment. Ultimately, the opensource release of the findings of this project will contribute to the broader community by setting new standards for AI security and promoting trust in AI technologies.

Algorithms and Software System for Analysis of Twitter Data using Apache Spark

The goal of this project is to develop a software system to collect, store, organize and query Twitter messages, and to develop algorithms that can process the Twitter data to extract value-added information, in particular, the geolocation of Tweets. First, we will design and implement a processing and analytics system for Twitter data using the Apache Spark environment. Second, we will research and extend advanced algorithms to infer the geolocation of Tweets from their contents. This will benefit Spotzi as they are developing and selling new media analysis products with a focus on geography-related applications. Novel use of the Spark environment will allow Spotzi to expand its services to a larger scale in terms of data sizes and processing capacity, by relying on the scalability of Spark as an efficient distributed computing environment. Additionally, since many of Spotzi’s products are related to geographic information, enhancing the geolocation information of Spotzi’s data is important for the accuracy of their analytics products.