UBC Researchers Advancing Next-Generation Sports Monitoring through Collaborative Research Funding from Samsung Electronics Canada
Source: https://bme.ubc.ca/ubc-researchers-advancing-next-generation-sports-monitoring-through-collaborative-research-funding-from-samsung-electronics-canada/ Parent: https://bme.ubc.ca/
Vancouver, BC — Researchers at the University of British Columbia are advancing a transformative new approach to sports and human performance monitoring through collaborative research funded by Samsung Electronics Canada to. The project, titled “Innovating a Next Generation of Sports Monitoring Technology” aims to develop novel wearable sensing systems and machine-learning algorithms capable of continuously and non-invasively monitoring muscle metabolic function and physiological performance during exercise. It is led by Dr. Babak Shadgan (Department of Orthopaedics; School of Biomedical Engineering; ICORD) and Dr. Ali Bashashati (School of Biomedical Engineering; Department of Pathology and Laboratory and is conducted in close collaboration with Samsung R&D Canada, reflecting a strategic partnership between UBC and Samsung in advanced digital health and sensor innovation.
Promising Early Results from Year One
In its first year, the project has already generated encouraging pilot data demonstrating the feasibility and potential clinical and performance relevance of this approach. Using synchronized wearable and laboratory-based sensors, including near-infrared spectroscopy (NIRS), photoplethysmography (PPG), electromyography (EMG), and metabolic reference systems, the research team successfully collected high-quality multimodal physiological data from healthy volunteers during incremental and endurance exercise protocols.
Most notably, the team has developed an initial machine-learning framework that estimates the muscle lactate threshold, a key physiological marker of exercise intensity and fatigue, from wearable NIRS signals alone. Early algorithmic evaluations show consistent localization of the metabolic transition point, with detection accuracy typically within 1–2 minutes of blood-lactate-based reference standards. These findings support the central hypothesis that local muscle metabolic changes can be detected non-invasively and may precede systemic blood lactate accumulation, offering a new window into real-time performance monitoring and fatigue detection.
Toward Human-Centered, Real-Time Performance Monitoring
By integrating physiological sensing with advanced signal processing and machine learning, this work addresses a longstanding gap in sports science and human performance monitoring: the lack of a direct, real-time measure of exercising muscle metabolism. The technology under development has potential applications extending beyond elite athletics, including rehabilitation, occupational health, military and aerospace human performance monitoring, and personalized exercise prescription.
“The early data strongly support the feasibility of this approach,” said Dr. Shadgan. “We are now positioned to move from proof-of-concept toward scalable, wearable solutions that can provide actionable physiological insight during exercise, without relying on invasive or impractical testing methods.”
A Milestone in Translational Biomedical Engineering at UBC
The research award was granted following a competitive, multi-stage internal selection process coordinated by UBC Innovation Partnerships in collaboration with Samsung Canada. The funding award from Samsung Electronics Canada highlights UBC’s growing leadership in translational biomedical engineering, wearable health technologies, and data-driven human performance research.
As the project enters its next phase, the team will focus on expanding participant cohorts, refining real-time algorithms, and developing custom wearable sensor prototypes, further strengthening the pathway toward real-world deployment and commercialization.
The work of Drs. Shadgan and Bashashati and their interdisciplinary research team highlights the potential impact of this UBC–Samsung collaboration on the future of human performance monitoring.