Innovative Projects Realized

L2M ParkinSense

This project aims to validate and strategically position an innovative digital health solution designed to monitor tremors in individuals with Parkinson’s disease. The system integrates a wearable sensor and a mobile application to provide continuous, objective measurements of tremor severity and patterns — data that are traditionally collected subjectively and infrequently during clinical visits. Over a four-month period, the project will pursue two complementary goals: technical validation and market validation. First, I will conduct a pilot study with people living with Parkinson’s disease to assess the device’s usability, comfort, and measurement accuracy compared to standard clinical assessments. Iterative improvements will be made based on user feedback to ensure clinical relevance and patient adherence. In parallel, I will conduct structured interviews and focus groups with neurologists, movement disorder specialists, and other healthcare professionals to understand clinical workflows, unmet needs, and integration points for the technology. Interviews with patients and caregivers will further elucidate symptom management challenges, willingness to adopt digital tools, and desired features. Insights from these activities will inform a comprehensive competitive landscape analysis and business strategy, including market size estimation, value proposition refinement, and a commercialization roadmap. By combining user-centered design, stakeholder engagement, and strategic market analysis, this project will not only validate the technical performance of the tremor-tracking system but also identify pathways for its adoption in clinical and home settings. The ultimate objective is to bridge the gap between innovative sensor technology and real-world clinical utility, enabling earlier intervention, more personalized care, and improved quality of life for people living with Parkinson’s disease. This project will lay the groundwork for future large-scale clinical trials and commercialization efforts, positioning the technology for meaningful impact in neurology care.

L2M – Designing and validating an AI-based, user-centered tool to assess discourse skills in individuals with aphasia

Up to 40% of stroke survivors can experience aphasia, a language disorder that affects a person’s ability to speak, understand, read, or write. However, one of the most effective ways to accurately assess aphasia, discourse analysis, remains underutilized. Discourse analysis is usually time-consuming and performed manually, leading to varying results between clinicians. It can also be difficult to integrate into busy clinical settings, leaving many stroke survivors in need of this assessment method without access. Practical assessments like discourse analysis are important because they help clinicians choose the most appropriate therapy options and track patient recovery more efficiently.

The purpose of this project is to develop an automated discourse analysis tool powered by Artificial Intelligence that is designed to assess linguistic difficulties in individuals with aphasia. This tool will be used to transcribe aphasic speech, extract its linguistic features, and deliver results in minutes, not hours. Designed by clinicians for clinicians, this tool can be easily used in busy clinical settings, providing clinicians with quick, accurate, and reliable assessments that can support diagnosis, treatment planning, and outcomes. By using this tool, clinicians may save time and reduce variability in their assessments, allowing them to focus on patient care. During this internship, we will conduct the first phase of the project, which includes engaging with potential users like speech-language pathologists, to gain insights into how to design and further develop this tool for the next phases of the project.

In terms of benefits at the health systems level, this tool may reduce wait times, optimize the use of resources, and improve the overall quality of rehabilitation services for patients. By making discourse analysis fast, consistent, and accessible, this tool could support its wider clinical use for individuals with aphasia.

MoCaP–ScoRe: Markerless Motion Capture for Precision Scoliosis Rehabilitation

Adolescent idiopathic scoliosis (AIS) is a common spinal condition that affects posture, movement, and quality of life. Deficiencies in proprioception, the body’s sense of position and movement, may play a role by disrupting balance and postural control, contributing to spinal misalignment.

Scoliosis is typically assessed with static images such as X-rays, which cannot show how the spine moves during daily activities. The most accurate technology for studying movement, marker-based motion capture, is costly, complex not allowing for routine clinical care.

This project will develop a safe, non-invasive, markerless system that uses machine learning (ML) trained on surface scans and X-ray images. The result will be an easy-to-use app that allows clinicians to assess posture, spinal movement, and postural control in real time.

In partnership with Curvy Spine, a specialty scoliosis clinic, the project will deliver a tool to track progress, personalize treatments, and measure the effects of exercises and bracing. This practical and scalable system will reduce reliance on repeated X-rays, improve clinical assessments and personalized intervention. Ultimately, it brings cutting-edge science into everyday practice to improve outcomes for young people with scoliosis.

L2M – SeeVita: AI-Powered Contactless Realtime Vital Sign Estimation

Accurate and accessible blood pressure monitoring remains a challenge for individuals, wellness providers, and organizations such as insurers that require reliable health data. Existing cuff-based devices are often inconvenient, intrusive, and unsuitable for continuous or large-scale use. There is a growing demand in the wellness, fitness, and insurance industries for technologies that make health monitoring seamless, affordable, and user-friendly.
Our project is developing a novel blood pressure monitoring platform that emphasizes non-invasiveness, usability, and integration into everyday settings. The concept is to provide a scalable solution that extends beyond clinical environments, supporting preventative health management and enabling new applications such as wellness tracking, risk assessment for insurers, and remote monitoring for organizations.

L2M – Multi-layered Collagen Scaffolds for Periodontal Regeneration

This project is developing a new type of collagen-based material that can help reattach loose teeth caused by severe gum disease or injury. When gum tissue and bone are damaged, teeth can loosen or fall out because the natural structures that anchor them are destroyed. Current treatments only partially repair this damage and often fail to restore proper teeth function. Our team has created a tri-layered collagen scaffold that imitates the natural layers of human dental support tissue, helping the body rebuild them in the correct order and restore teeth attachment. Through the Lab2Market program, we will explore how this innovation could be brought from the lab into dental clinics by studying market needs, pricing, and adoption barriers. This work will help the partner organization advance its mission of turning Canadian research discoveries into real-world health innovations that improve patient outcomes and reduce long-term dental treatment costs.

L2M – NexusNeuro Portable EEG Validation

Diagnosing neurological conditions like epilepsy is often difficult because clinicians rely on brief, in-clinic brain activity recordings, which may miss crucial events. To solve this, our company, NexusNeuro, is developing a “Neurological Holter Monitor” — a discreet, wearable EEG device that allows for continuous, long-term brain monitoring in a patient’s everyday environment. While we have the core technology, we need to confirm the best way to bring it to market.

This four-month project is designed to bridge that gap. The goal is to perform a deep market validation study to determine the most urgent clinical need for our device. This will be achieved by conducting over 100 interviews with key stakeholders, including neurologists, sleep specialists, and hospital administrators.

The primary benefit for NexusNeuro will be the creation of a Commercialization Blueprint. This key deliverable will use the data gathered from interviews to define the essential features for our first product, outline a clear go-to-market strategy, and validate our business model. By completing this project, we will transform our innovative idea into an evidence-based business plan, significantly reducing risk and accelerating our path to improving patient care in Canada and beyond.

L2M- Lightweight Additive Manufacturing of Marine Components for Fuel Efficient and Low-Emission Shipping

The proposed project aims to develop advanced materials that can better resist corrosion and damage in marine and offshore environments. These materials will help extend the service life of equipment and structures exposed to harsh seawater conditions, reducing maintenance costs and downtime. By improving durability and reliability, the project will support safer and more efficient operations for the partner organization and contribute to more sustainable marine infrastructure management.

TRLUP-MPWash

MPWash develops a reusable, cartridge-free filter that captures microplastics released during laundry. The project advances prototype validation, regulatory readiness, and commercialization to reduce pollution and strengthen Canada’s cleantech leadership.

TRLUP – GlucoSmart Nanogel – a smart bioactive nanogel for healing diabetic foot ulcers

This project focuses on developing a market and regulatory strategy for GlucoSmart Nanogel, an innovative Canadian-made wound-healing therapy designed for people with diabetes. The nanogel combines infection control, immune modulation, and tissue repair in a single, affordable treatment to improve healing outcomes for diabetic foot ulcers. Over four months, the project will evaluate regulatory requirements, market opportunities, and reimbursement pathways to create a clear roadmap for bringing GlucoSmart Nanogel to market and advancing its real-world impact in diabetic wound care.

TRLUP -VerteVUE Spinal Imaging

VerteVUE is a portable, radiation free, imaging device that will allow clinicians to assess critical anatomical features of the spine at the point of care. VerteVUE supports early clinical decision making when used alongside patient reported outcome measures (PROMs), standardized questionnaires and physical assessments. It also streamlines triage to determine conservative versus surgical care, reducing patient wait times and setting a new standard for precision spinal care.

AI-assisted image segmentation and registration for spine navigation systems

Spine surgery often requires real-time X-ray images to guide surgical tools safely around delicate structures. However, using X-rays continuously during long operations can expose both patients and medical staff to unnecessary radiation. One way to reduce this risk is to use saved X-ray images for navigation rather than continuous exposure; however, this requires precise tool tracking to ensure accurate guidance. ClaroNav Kolahi Inc. (CKI) is a Canadian company that develops surgical navigation systems, including a spine navigation platform that can operate from a saved X-ray image. In this project, the intern will help design a method that uses small reference markers placed on the X-ray machine and visible to a camera-based tracking system, along with artificial intelligence–based image analysis to automatically link the position of surgical tools in real time with that saved image. This ensures navigation remains accurate even if the patient moves or if the image is rotated or scaled. For CKI, this innovation will make their navigation system more reliable and compatible with a wider range of hospitals, while also reducing radiation risks for patients and surgical teams.

TRLUP – AI-Powered Drones for Infrastructure and Industrial Inspection

Aeronovous delivers AI-powered drone inspection solutions for infrastructure, industrial equipment, and wind turbines, with this internship specifically focused on wind turbine applications. The traditional inspection industry relies on costly and inefficient manual methods—rope-access technicians, prolonged turbine outages, and highly variable reports influenced by crew experience and weather conditions—creating significant safety risks, operational downtime, and inconsistent defect detection. Even when drones are deployed, human operators must manually review thousands of images to identify defects, a tedious and error-prone process that can allow critical issues to go unnoticed, leading to equipment degradation and expensive failures. Aeronovous differentiates itself through automated defect detection powered by computer vision, deploying a drone workflow that captures standardized imagery of turbine blades, towers, hubs, and nacelles, then automatically detects, measures, and tracks defects, reducing days of manual analysis to hours of automated processing and enabling operators to schedule repairs with minimized downtime, cost, and risk. Currently in a pre-commercial stage, the core technology has been validated in simulation environments but awaits real-world validation with paying customers in operational settings. To achieve commercialization, the company must establish reliable sensor integration, develop repeatable capture protocols for tall structures, generate labeled datasets tailored to client needs, train models meeting accuracy benchmarks under field conditions, and ensure compliance with Transport Canada regulations and site safety procedures—all while demonstrating measurable return on investment to secure early adopters for pilot programs. This project directly tackles these commercialization barriers through market validation and customer discovery, defining turbine inspection requirements and safety protocols, standardizing image capture workflows using the RGB FPV drone platform, establishing infrastructure for data storage and labeling, training and benchmarking defect-detection models, developing standard operating procedures and ROI frameworks, and securing pilot agreements with prospective clients to validate the technology in live operational environments.