Who are we?

Professor

Dr. Qingguo Li is a Full Professor in the Department of Mechanical and Materials Engineering (MME) at Queen’s University, where he leads research focused on wearable robotics, human movement monitoring, and energy harvesting technologies. His work develops advanced exoskeletons and sensors aimed at enhancing human mobility, preventing injuries, and supporting human augmentation. Dr. Li's work has been published in high-impact journals, including two articles featured in Science. Dr. Li has been a visiting professor at Zhejiang University (2015–2016) in China and at the Indian Institute of Technology, Roorkee (2023–2024) in India. He was honored with the Governor General’s Gold Medal in 2006 for outstanding academic achievement. He holds several US and Canadian patents and has co-founded Bionic Power Inc., a company specializing in energy harvesting and exoskeleton systems. Outside work, Dr. Li likes gaderning, reading, and swiming.

Ph.D. Student

Samuel Brost is a PhD student in the Mechanical and Materials Engineering Department. Sam began working in the BMRL lab as an undergraduate researcher with an NSERC USRA in 2020. His research focuses on collaboration with workers in an effort to reduce work-related musculoskeletal disorders. Part of his research has focused on analyzing biomechanics factors for injuries of weavers in Burkina Faso. His master’s research focused on the development of a novel wearable system for modelling spinal loading during healthcare procedures. His current work looks at the development of a multi-faceted intervention for reducing loading in healthcare workers wearing lead aprons including wearable feedback, soft exoskeleton garments, and a passive lead apron weight distribution system. Outside of the lab, you can find Sam playing volleyball, reading, or enjoying the outdoors.

Ph.D. Student

Paul Quinlan is a second-year Ph.D. student in the Electrical and Computer Engineering department at Queen’s University, co-supervised by Dr. Qingguo Li and Dr. Xiaodan Zhu. His research focuses on the development of large multimodal language models, particularly in the context of time-series analysis. With a strong background in artificial intelligence, he has worked extensively on integrating language and reasoning into time-series applications, contributing to cutting-edge advancements in machine learning and AI. Paul has published multiple research papers and presented at esteemed conferences, showcasing his expertise in AI for sensor data and multimodal learning. His expertise extends to machine learning, deep learning, and practical applications in both research and industry settings. Outside of his academic pursuits, Paul is passionate about sports, particularly hockey and golf.

MASc Student

Will Bonin is a second-year MASc student in the Mechanical & Materials Engineering Department at Queen’s University, supervised by Dr. Qingguo Li. His research focuses on investigating work-related musculoskeletal disorders (MSK) in healthcare workers within surgical environments. His work aims to assess the prevalence of these disorders in the operating room, identifying key biomechanical risk factors that contribute to strain and injury. By integrating motion analysis techniques and wearable sensor technology, he explores how Exo-Sensory devices can reduce physical demands on healthcare professionals, ultimately improving workplace ergonomics and long-term health outcomes. Beyond his research, Will has contributed to engineering education through Reimagine Engineering by Smith Engineering, where he helped develop hands-on content for fourth-year biomechanical engineering students. He has served as a teaching assistant in MECH496 (Musculoskeletal Biomechanics) and as a teaching fellow in MECH350 (Automatic Controls), furthering student engagement in applied biomechanics and control systems. Will is passionate about wearable devices, competitive sailing and hockey.

Graduate Student

Natasha Anderson is a fourth-year Mechanical Engineering student specializing in the Biomechanical sub-plan at Queen’s University. Her research focuses on understanding the relationship between skin pressure and strap tension for the control of rehabilitative devices. Specifically, she aims to characterize the relationship between pressure distribution on the skin and strap tension and determine whether this relationship remains consistent across individuals. The findings from this study will be used to inform the design of automatically controlled rehabilitative devices, such as knee braces that regulate pressure and compression based on the type and severity of an injury. Outside of academics, Natasha enjoys reading and doing puzzles.

Undergraduate Student

Jialin Luo is a first-year electrical engineering student currently conducting design work under Dr. Qingguo Li. Her past projects include illustrating proposal figures of surgical ergonomics and textiles, redesigning diagrams on operating postural risks, and creating the Bio-Mechatronics and Robotics Laboratory logo. She is passionate about applying engineering and design to healthcare challenges, with a growing interest in embedded systems for medical devices and wearable technology. Beyond engineering, Jialin is a provincial award-winning pianist, a Canada-Wide Science Fair bronze medalist, and a freelance artist commissioned by clients across Canada, the US, and New Zealand.

Undergraduate Student (2025)

Sydney Novecosky is a Mechanical Engineering student specializing in the Biomechanical sub-plan at Queen’s University. She recently completed a 16-month internship focused on automating manufacturing processes in autonomy and positioning, gaining hands-on experience in cutting-edge industrial technologies. Sydney’s research focuses on comparing the kinematic measurements of joint and trunk angles between two motion capture systems—Xsens and OpenSim—during handloom weaving. Through data collection with weavers from the Kingston Handloom Weavers & Spinners Guild, she aims to assess the accuracy and reliability of these systems using statistical analysis, including Bland-Altman and correlation analyses. Her findings will help validate the use of cost-effective, accessible tools like OpenSim in resource-limited settings, such as Burkina Faso, where musculoskeletal disorders (MSDs) among weavers are prevalent. This research contributes to improving biomechanical assessment methods and developing ergonomic interventions to enhance worker safety. Outside of academics, Sydney enjoys staying active and embracing the outdoors. Whether it’s going for a run along scenic trails, hiking through rugged landscapes, or simply spending time in nature, she finds inspiration and balance in physical activity. Her love for movement extends beyond her studies, as she is always eager to explore new environments and challenge herself through outdoor adventures. Upon completion of her degree, she is looking to work in the medical device manufacturing field.

Undergraduate Student (2025)

Emily Peetsma is a Mechanical Engineering student in the BioMechanical sub-plan at Queen’s University. Returning from a 16 month internship as a Process Engineer she is currently in her final year of undergraduate studies. Her research looks at using machine learning techniques to process OpenSim data to classify movements and predict the risk of musculoskeletal disorders (MSDs). Emily’s study proposes integrating supervised & unsupervised learning algorithms to classify repetitive weaving motions, identify high-risk postures and predict MSD risks. By leveraging machine learning, her aim is to enhance the ability to extract meaningful results from motion capture data, supporting ergonomic interventions for injury prevention in weavers. Her goal is to provide valuable insights into the application of low-cost, accessible motion analysis tools for studying human movement and predicting MSDs in resources limited settings. Aside from academics Emily enjoys playing golf, skiing, swimming, and knitting. Upon completion of her degree, she is looking to work in the medical device manufacturing field.

Undergraduate Student (2025)

Hilary Bassi is a fifth-year Biomechanical Engineering student at Queen’s University. Her research focuses on developing a motion classification and repetition tracking system using wearable motion sensors and machine learning. This system has the potential to be used in the future for at-home rehabilitation, particularly for patients with cognitive impairments who struggle with exercise adherence. Existing rehabilitation methods often lack real-time feedback outside of clinical settings, making at-home recovery more challenging. By refining the accuracy and reliability of motion tracking, Hilary’s work lays the groundwork for future applications in rehabilitation technology. With a strong interest in the medical field, Hilary aspires to integrate engineering expertise with patient care by designing medical devices that enhance the quality of life for individuals with disabilities and chronic illnesses. Outside of academics, Hilary enjoys going to the gym, cooking, reading, and spending time with dogs.

Undergraduate Student (2025)

Eden Duffy is a Mechanical Engineering student specializing in the BioMechanical sub-plan Queen’s University. Her research focuses on leveraging OpenSim software to capture and analyze repetitive movements in traditional handloom weaving. Through force and kinematic analysis, she aims to identify movement patterns that may contribute to musculoskeletal disorders (MSDs). Her findings, along with an evaluation of OpenSim’s limitations, will be shared with researchers in Burkina Faso to support the development of preventative measures for MSDs. Outside of her research, Eden enjoys crocheting, playing sports, and going to the gym.

Undergraduate Student (2025)

Jonah is a 5th year General Option Mechanical Engineering student at Queen’s University. His research focuses on applying DeepLabCut™, an adaptive biomarker pose estimation software, to track the motion of weavers in Burkina Faso. This information is being used to identify specific positions that give high RULA scores and thus have increased potential for musculoskeletal damage. Prior to this research project, Jonah worked as a data analyst at both Labatt and RBC, applying machine learning regression models to derive insights from consumer and financial datapoints. Outside of academics, Jonah is passionate about music and sports, particularly guitar and water skiing. Upon completion of his degree, he will be working in the UK as a commercial data analyst.