Design and Innovation Day 2026

Team 1 – Magnetic Hydrogel Adhesive for Improved Surgical Wound Healing 

Project Description:

This project explores the development of a magnetic hydrogel as an alternative to traditional sutures for closing surgical wounds. Our client previously created a Printable Tough Adhesive (PTA) hydrogel that strongly adheres to skin while maintaining a moist environment, which is known to support tissue healing. However, a practical application for this material had not yet been established. After evaluating several possibilities, our team identified surgical wound closure as a promising use.  

To enhance the hydrogel’s functionality, we incorporated iron oxide particles, enabling it to respond to external magnetic fields. This feature allows for controlled positioning and improved contact with wound surfaces. The modified hydrogel was extensively characterized and tested on ex-vivo tissue models, where it consistently demonstrated the ability to effectively close wounds.  

Overall, our magnetic hydrogel offers the potential for a simple and rapid alternative to sutures, with the added benefits of promoting healing through moisture retention and gentle mechanical stimulation. This approach has the potential to reduce procedure time and improve patient comfort in surgical settings.

Team Members: Destin Du, Alicia Fung, Ilana Guez, Stella Kauryzhka

Client: Zhenwei Ma, Assistant Professor, Ma lab at the University of British Columbia

Team 2 – Design of an EMG-Based Upper-Limb Amputee Rehabilitation & Control

Project Description:

Upper-limb amputation significantly impacts an individual’s ability to perform everyday tasks. Although prosthetic devices can help restore function, traditional control schemes are often unintuitive. Most rely on simple “on-off” methods, and can only detect when the user intends to open or close their hand. To perform other actions, users must perform a sequence of pre-defined muscle activations (e.g. open-open-close mapped to thumbs-up). As the desired number of actions increases, activation sequences become more cognitively demanding, leading to user frustration and reduced device adoption.

Additionally, many patients experience long delays before being fitted with a prosthetic, and there are no standardized protocols for post-operative rehabilitation and pre-prosthetic training. This can slow recovery and reduce long-term success.

Our project addresses these challenges with a virtual rehabilitation and control platform that uses machine learning for gesture recognition. The system adapts to each user’s unique muscle signals and ability levels, allowing for more intuitive control and enabling earlier, at-home training. Our target group is transradial (below-the-elbow) amputees, allowing us to leverage their intact muscles to capture signals that naturally correspond to intended hand and finger motions, resulting in more intuitive prosthetic control.

Team Members: Maline Akwar, Kate Roett, Alyshia Soans, Xinyan Wen

Client: Fraser Douglas, Research Assistant, Human Motion Biomechanics Lab (HuMBL)

Team 3 – A Proof-of-Concept Fabric EMG Monitoring Platform

Project Description:

Our project addresses barriers to the widespread clinical adoption of high-density surface electromyography (EMG) by providing a wearable textile validation platform as a replacement for conventional adhesive electrodes. By eliminating the need for skin preparation and conductive gels, this design makes EMG technology more accessible for frequent, repeated clinical use. Focusing on stroke rehabilitation patients, we developed a wearable for addressing common mobility deficiencies in the tibialis anterior muscle, and a cross-platform software application for data acquisition and analysis. The wearable facilitates targeted application of an EMG electrode array and communicates via biosignal amplifier. The accompanying software application delivers real-time data acquisition and post session analysis of clinically relevant metrics, such as activation timing, bilateral symmetry, and time to fatigue. The app has been developed for both desktop and mobile use, for more flexibility in its use. The design is built as a framework and serves as a foundation for continued hardware and software development by Focal Lines

Team Members: Rhett Sawyer, Risha Reddy, Nick Santoso, Jaswinder Brar

Client: Charlie Vu, Managing Director, Focal Lines Technologies

Team 4 – Seaweed-Based Wound Healing Bandage

Project Description:

Burn wounds present a clinical challenge due to their susceptibility to infection, fluid loss, and prolonged healing times. Conventional wound dressings, such as Band-Aids, are typically made from non-biodegradable materials that contribute to increasing medical waste and have been shown to contain carcinogenic PFAs (Per- and polyfluoroalkyl substances). Additionally, these dressings often adhere to fragile, regenerating tissue, leading to pain and potential re-injury upon removal (Image A). Importantly, they serve a primarily passive role, offering protection without actively supporting an optimal healing environment.

In this project, we present a biodegradable seaweed-derived hydrogel designed for burn care. The material exhibits soft adhesion to the wound surface, allowing it to remain securely in place while enabling gentle removal (Image B). The hydrogel supports a moist healing environment and provides a protective barrier against contaminants, both critical for effective burn recovery. Unlike conventional dressings, the use of naturally derived components enables the material to degrade under typical disposal conditions, reducing environmental persistence.

By integrating biocompatible material design with environmental sustainability, this approach addresses key limitations of existing wound care products. This solution has the potential to improve patient comfort and healing outcomes while reducing the environmental impact associated with single-use medical materials.

Team Members: Karina Akhmedova, Melanie Cheng, Haoyang Guan, Theo Nguyen

Client: Miha Biotech

Team 5 – Ultrasound Probe Stabilization During Cardiac Resuscitation

Project Description:

During cardiopulmonary resuscitation (CPR), every second matters. Current pulse checks rely on manual palpation, which can be difficult to perform accurately and quickly which affect CPR efficiency and patient outcomes. Alternatively using a cardiac ultrasound reliably detects pulse but requires an additional clinician to operate the probe. To address this, our capstone team, in partnership with an emergency physician, developed an ultrasound probe stabilization device designed to support reliable CPR monitoring. The device was designed for fast application, adaptability across a wide range of patient body types, smooth probe adjustability, and dependable stability for clinical integration.

The system uses a two part stabilization method: A gel adhesive base to secure the device to the patient and dampen motion along with a tension tape system applied in opposing directions to help the device conform to different body shapes. A ball-joint probe holder then allows multi-axis adjustment for accurate placement. Once positioned, a friction lock collar is pushed down to secure the whole system. Integrated force sensors at critical regions on the base provide real time visual feedback on stability.

By reducing reliance on manual holding, this design supports more reliable ultrasound monitoring during CPR while reducing burden on medical personnel.

Team Members: Brianna Tsuyuki, Heena Sharma, Zhipeng Ren, Molin Li

Client: Dr. Amir Behboudi, Emergency Physician, Peace Arch Hospital

Team 6 – Rapid Detection of Multiple Cardiac Biomarkers through Nanomaterial-Based Blood Filtration

Project Description:

This project aims to produce a quick and cost-effective multi-biomarker panel for point-of-care blood diagnostics. The current gold standard method involves centrifugation to isolate plasma, followed by a full chemistry analysis. This process is lengthy, costly, and requires highly specialized equipment that needs to be operated by trained personnel. Paper-based lateral flow assays (LFAs) are a cost-effective, rapid diagnostic tool well-suited for point-of-care settings; however, they lack the sensitivity and specificity required for comprehensive diagnosis, including the ability to simultaneously detect the multiple protein biomarkers needed to support a well-informed clinical conclusion. This drives up blood volumes and delays treatment.

Our project utilizes graphene oxide (GO) as a filter, localizing blood plasma separation and providing greater control over sample flow distribution across multiple channels. In this project we were able to manufacture a 3-armed LFA that successfully separated plasma and retained cardiac proteins. Our device requires less than a third of the blood needed for three separate LFA tests and delivers results within minutes. This enables healthcare providers, from paramedics to ER physicians, to make timely, informed decisions about lifesaving treatment.

Team Members: Nora Kotkas, Raghav Madhwal, Neil Mitra, Maggie Wang

Client: Dr. Boris Stoeber, Professor, UBC Mechanical Engineering

Team 7: NIMRRad: Non-Invasive Monitoring of Radiation in the Radial Artery

Project Description:

Our project NIMRRad (Non-invasive monitoring of radiation in the radial artery) aims to develop a compact, wearable device capable of measuring radiation levels in the bloodstream.

Cancer patients are often treated with radiopharmaceutical drugs that use radioactive isotopes to eliminate cancerous cells. In order to enable accurate drug dosing, it is critical to monitor how these drugs move through the body. Unfortunately, current clinical methods rely on invasive blood draws that are invasive, painful and time-intensive.

Our device, NIMRRad, aims to eliminate the need for blood draws, through a wrist-worn sensing system that detects gamma radiation from the drug as it passes through the radial artery. The device uses scintillaton crystals that work to convert the emitted gamma radiation into flashes of light. These flashes are then detected by a silicon photomultiplier that converts the intensity of light to a proportional electrical signal. These signals, once denoised, amplified, and processed, enable precise characterization of changes in arterial radiation levels over time.

With non-invasive monitoring, there is potential to simplify clinical workflows, improve patient outcomes and enable continuous monitoring of radiopharmaceuticals. NIMRRad provides a low cost alternative that could make radiation-based diagnostics more accessible and efficient.

Team Members: Avi Pandya, Matin Narimani, Rohan Arvind, SJ Patel

Client: Dr. David Liu, Dr. Arman Rahmim, and Dr. Carlos Uribe, School of Biomedical Engineering and BC Cancer

Team 8 – Next-Generation Air Filtering Mouthpiece Sensor Miniaturization

Project Description:

Air pollution poses a significant risk to the health of residents in urban areas, especially athletes due to increased breathing rate and switch from nose breathing to mouth breathing. Inhaling particulate matter (PM) smaller than 2.5 microns can cause adverse health effects such as reduced cardiovascular and respiratory function, inflammatory responses, reduced athletic performance, and premature morbidity.

Our goal is to create a comfortable and portable air monitoring and filtration device that informs athletes when they are at risk of inhaling dangerous amounts of PM and harmful gases. To do so, we designed a miniaturized air quality sensing PCB and associated app, measuring and displaying real-time PM concentration, humidity, temperature, gas, and pressure to users. Our PCB will later be integrated into the aesthetic housing design created by the UBC Manufacturing team. Together, our system not only filters the air but also provides real-time air quality information, protecting athletes from harmful pollutants and informing them about the air they breathe.

Team Members: Laura Siemens, Andrew Paul Anand, Grace Harms, Reza Sabid

Client: Sebastien Chabot, The Collective Energy Foundation

Team 9 – A Smart Wearable Device For Bruxism Monitoring and Biofeedback

Project Description:

Sleep bruxism, or unconscious teeth grinding and clenching during sleep, affects many young adults and can lead to headaches, jaw pain, tooth damage, and poor sleep quality. Current solutions, such as mouthguards or medications, mainly focus on protecting teeth or reducing symptoms, but they do not help users understand or change their behavior over time.

Our project introduces a wearable device that monitors jaw muscle activity during sleep and aggregate insights for downstream clinical review, aiding formal diagnosis. The device uses small, skin-safe EMG sensors placed on the temporalis muscles and is integrated into the familiar form factor of a sleep mask, maximizing comfort and ease of use. By combining real-time data acquisition with machine learning algorithms, the system can identify clenching patterns specific to various users.

The direct outcome of this project is a functional prototype that demonstrates reliable detection of clenching events in a wearable format. This approach has the potential to shift bruxism care from passive protection to active awareness and behavior change, enabling users to better manage their condition and reduce long-term health impacts.

Team Members: Muhammad Al Muhtadin, Emily Chen, Anna Gleason, and Albin Soni

Client: Dr. Om Bhatt MD, Associate Professor, UBC/Fraser Health

Team 10 – Artificial Intelligence Application for Automated Karyotyping in Clinical Genetic Testing

Project Description:

A karyogram is a visual arrangement of all 46 chromosomes, ordered as pairs 1 to 23, with the short arm (p-arm) facing upward. It is an essential tool for diagnosing and treating genetic disorders such as leukemia and Down syndrome. Our client, Dr. Li, a cytogeneticist at Vancouver General Hospital, identified that creating a karyogram is currently a time-consuming and highly manual process. This project aims to automate the end-to-end karyogram generation workflow. The user only needs to upload a metaphase image of a patient’s chromosomes. The pipeline first classifies the chromosomes using a YOLOv5 model with 98% accuracy. The resulting bounding boxes are then cropped, and each chromosome is segmented using masking. These masks are passed into an alignment algorithm that rotates each chromosome, so its longest axis matches the y-axis of the frame, effectively straightening it. Finally, the p-arm is identified using a combination of CVAE and linear classifier models, and the chromosome is flipped if needed so the p-arm faces upward. The user interface of the app also includes tools such as an eraser and corrector, making manual corrections quick and simple when needed.

Team Members: Setare Maleki Rizi, Saatvik Kurap, Pierce Alikusumah, Hanna Khan

Client: Dan Li, Cytogenesis, Cytogenetics Laboratory, Vancouver General Hospital

Team 11 – High Speed Helmet Testing Using OIBG Drop Rail

Project Description:

Helmets are critical for protecting people in activities with fall/crash potential; however, current helmet testing methods are not able to achieve the higher tangential speeds seen in many accidents. The growing use of e-scooters and motorcycles, along with high potential fall height in industrial work, creates an increasing need to evaluate helmet performance at higher speeds.

Our project was to retrofit a helmet test drop rail at the Vancouver General Hospital’s Orthopaedics and Injury Biomechanics Group (OIBG) to reach higher impact speeds. While the original drop rail was limited to 7.3m/s, our retrofit enables testing between 10.8 and 13m/s, which corresponds with high fall height (6 – 9m) and high speed crashes.

To achieve this safely in the enclosed indoor lab space, we designed a novel flywheel-based acceleration system that avoids storing large amounts of potential energy above users. Counter-rotating wheels driven by motors surround the rail and accelerate the helmet-carrying carriage while it is dropping.

This design allows the research engineers at OIBG to better study helmet performance at higher speeds, to improve safety standards, helmet design, and testing methods, and will lead to more effective protective equipment for higher danger scenarios.

Team Members: Ali Hawkins, Sophia Katramadakis, Lauryn McKenzie, Evan Wong

Client: Vivian Chung, P.Eng. Research Engineer at the Orthopaedic and Injury Biomechanics Group

Team 12 – Dr. Wobbles: Pain Relief Device for Pediatric Injections

Project Description:

Our project aims to develop a pain relief device for pediatric patients undergoing repeated injection procedures. The device is designed for clinical use and helps reduce procedural pain and anxiety for children while supporting healthcare professionals in delivering more comfortable and efficient care.

Repeated painful injections are a significant concern in pediatric medicine, with conditions like Juvenile Idiopathic Arthritis (JIA) affecting approximately 6,200 children aged 0–15 in Canada alone. JIA is a chronic rheumatic disease requiring ongoing injection-based treatment, where pain management remains a persistent challenge. Repeated procedural pain can cause lasting psychological trauma and reduce patient adherence to necessary treatment.

We integrated vibration and cooling mechanisms in a modular, 3D-printable housing that can be disinfected for repeated use. Our goal is to create a practical, affordable, and clinician-friendly device that meaningfully reduces perceived pain and improves quality of life for pediatric patients throughout their treatment journey.

Team Members: Kathleen Botha, Jordan Thompson, Myra Wei, Lauren Young

Client: Dr. Brian Greeley, Research Project Manager, The Digital Lab

Team 13 – Wearable Assessment System for Children with Walking Difficulties

Project Description:

Many children with neurological conditions have difficulty walking, which can limit their independence and quality of life. Conditions such as cerebral palsy, a leading cause of walking difficulties in children, affect about 2 to 2.5 per 1000 births in Canada. These children may develop unusual walking patterns, such as walking on their toes, which affect how they move and maintain balance. Current systems are expensive, complex, and mainly available in specialized laboratories. As a result, many families, especially in rural communities, face challenges accessing timely assessment and care.

This project aims to develop an affordable, accessible, and easy-to-use system to assess how children walk, allowing use in more clinical settings. The system uses lightweight, wearable sensors to measure key walking characteristics: walking speed, step length, and steps per minute. These measurements give clinicians clear, easy-to-understand information to support diagnosis, treatment planning, and tracking patient progress.

The visual shows an overview of the system, which uses affordable, widely available components and a simple app for data visualization. This helps healthcare providers interpret results and make informed decisions. By expanding access, this project aims to enable earlier intervention, improve clinical decisions, and enhance long-term outcomes for children with mobility challenges.

Team Members: Daniela Guevara Giraldo, Kiana Jahanshahi, Lujine Younis, Theon Mascher

Client: The Digital Lab

Team 15 – Scale-down model of bioreactor culture for stem cell differentiation

Project Description:

Modern cancer treatments, such as CAR-T cell therapy, rely on the mass production of specialized immune cells. Traditionally, these T cells are grown in large “stirred tank” bioreactors, with continuous nutrient exchange (media perfusion) systems demonstrating the best yields of cell products. However, testing new ways to improve cell growth and differentiation at such a large scale is expensive and time-consuming.

Our team developed a scaled-down model that brings this process from a large tank to a standard 24-well cell culture plate. Using a microfluidic approach, our system implements perfusion found in large-scale reactors. This setup allows researchers to rapidly test many different media “recipes” simultaneously. Specifically, our project focuses on the critical transition where blood stem cells begin their journey toward becoming functional T cells.

By providing a high-throughput platform for experimentation, this device helps researchers optimize cell culture conditions more efficiently. Ultimately, these small-scale discoveries will enable more effective, large-scale production of life-saving cell therapies.

Team Members: Daniel Hinatsu, Laura Ing, William Ji, Ronik Sharma

Client: Richard Carpenedo, Bioprocess Engineer, UBC SBME

Team 16 – RapidAST: Optical Detection of UTI Bacterial Growth Rates for Rapid Antibiotic Susceptibility Testing

Project Description:

Urinary tract infections (UTIs) are among the most common bacterial infections worldwide, and the growing prevalence of antibiotic-resistant UTIs continues to challenge effective treatment. Antibiotic susceptibility testing (AST) addresses this issue by identifying the most effective antibiotic for a patient’s specific infection. However, conventional AST methods are often slow, labour-intensive, and dependent on centralized diagnostic laboratories, leading to delays in treatment and greater reliance on broad-spectrum antibiotics.

In collaboration with our client, we are developing a novel optical imaging solution to rapidly assess antibiotic susceptibility in urine samples. Our project uses digital inline holography, a high-resolution 3D imaging technique, to visualize E. coli directly within the sample. By illuminating the sample with a laser and capturing the resulting holograms on a camera, our system enables computational reconstruction of bacterial structure and behaviour for analysis. This approach supports rapid, label-free imaging without requiring additional reagents or complex sample preparation.

Team Members: Maya Ansu, Sarv Brar, Bren Klein, Harrison Kim

Client: Faisal Khan, CEO, FMRK Diagnostic Technologies Inc.

Team 17 – Context-Aware and Efficient Data Valuation for Medical Machine Learning

Project Description: Training data is the foundation of machine learning, yet not all data points are equally useful. As models saturate in performance and as noisy or AI-generated content (“AI slop”) proliferates, the need for principled methods to quantify the value of individual samples is more pressing than ever. Our capstone project explores and benchmarks data valuation techniques such as Shapley Values and Optimal Transport (OT) for machine learning pipelines, with an emphasis on medical imaging datasets. Data valuation assigns importance values to data points in a dataset to identify the most and least useful pieces of data contributing to training a machine learning model. Our goal is to provide the community with open implementations and evaluations that enable the following: efficient data preparation for costly ML annotation pipelines; identification of mislabeled, redundant, or harmful samples; task-aware data valuation for multi-task and medical ML models; exploration of group-wise effects (synergistic or antagonistic). Ultimately, this project is about enabling better machine learning models with less data without compromising rigor or reproducibility. Beyond model performance, data valuation is a sustainability lever. Training on smaller, higher-quality subsets means fewer GPU hours, lower energy consumption, and reduced carbon emissions without sacrificing model performance.

Team Members: Amy Yu Chloe Christensen Dhairya Aggarwal Jaiden Siu

Client: Dr. Rohit Singla, MD PhD Postdoctoral Fellow, Resident Physician UBC Robotics and Control Laboratory

Team 18 – Kidney Graft Failure Trajectory Model

Project Description:

Chronic kidney disease places a major burden on healthcare systems, and kidney transplantation remains the best treatment for many patients with end-stage renal disease. In British Columbia, post-transplant monitoring is especially demanding, with only eight trained transplant physicians overseeing roughly 5,000 kidney graft patients. Our project aims to support this process by developing a clinical decision-support tool that helps estimate a patient’s risk of kidney graft failure before and after transplant. We explored several data-driven approaches, including survival models, longitudinal statistical models, and machine learning methods. Based on the needs of the problem and the structure of the data, our final design uses a multi-model approach that separates the graft journey into key stages of care, allowing risk to be assessed in a way that better matches the clinical setting. Our user-friendly interface also allows clinicians to review patient information and identify patients that may need closer monitoring. By helping care teams recognize risk earlier, our tool supports more informed decision making and better long-term care for kidney transplant patients.

Team Members: Yasmine Bellahcen, Nadège Oger, Alan Khakimov, Adly Al-Sharif

Client: Karen Sherwood, Clinical Associate Professor, Vancouver Coastal Health (BC Transplant)

Team 19 – Breast Lesion MRI Segmentation & Classification

Project Description:

Breast cancer is one of the most common cancers worldwide, and early detection plays a critical role in improving patient outcomes. In this project, developed in collaboration with researchers at the University of British Columbia, we explored how artificial intelligence (AI) could help analyze breast scans and support clinicians in identifying potential cancerous tumors.

Our team developed an AI system that analyzes breast scan images in two steps. First, the system detects and outlines suspicious regions that may contain a lesion. It then evaluates those regions to determine whether the lesion is more likely to be benign (non-cancerous) or malignant (cancerous).

In preliminary testing, the system successfully identified lesion regions with mean of 84% overlap with expert annotations, meaning the model’s detected areas closely matched those identified by radiologists. The system also achieved approximately 82% accuracy in distinguishing benign (non-cancerous) from malignant (cancerous) lesions.

The tool is intended to support radiologists and serve as a supportive screening tool that highlights areas of concern and helps clinicians review scans more efficiently, particularly in healthcare settings where specialized expertise may be limited.

Team Members: Scott Feng, Trevor Liu, Renata Lawrence, Joan Nwokeforo

Client: Dr. Ilker Hacihaliloglu, UBC Dept. of Radiology & Medicine

Team 20 – Automated Ultrasound Sweeping System for Operator-Independent Use of Liver Diagnostic Devices

Project Description:

Our client, Sonic Incytes Medical, is beginning production of the Velacur One, an ultrasound liver diagnostic device, and wants to improve their quality control pipeline. Their current validation scheme requires a trained technician to manually test each system on an artificial liver, called a phantom, before shipping, which consumes valuable time and manpower. Our solution, called the Automated Probe Tester (APT), automates this process thereby increasing the reliability and repeatability of each test.

Our device consists of two components, the first being a height adjustable system which holds the ultrasound probe in place and can accommodate different sized phantoms. The second component is the control box and motor setup which precisely controls the rotation of the ultrasound probe during testing and shields the readings from electromagnetic interference caused by the electronics.

The APT has been tested and validated to perform as well as a trained operator, ensuring consistent, repeatable measurements while reducing user-dependent variability.

Team Members: Brian Chau, Edward Musete , Justin The, Nasif Inan Chowdhury

Client: Julio Lobo, VP Research and Development, Sonic Incytes Medical Corp.

Team 21 – Wheelchair accessory to reduce lower extremity injury in patients with advanced Alzheimer’s and dementia

Project Description:

Individuals living with advanced dementia or Alzheimer’s disease often retain an involuntary walking reflex even after losing the ability to walk independently. When seated in a manual wheelchair, this reflex can cause their feet to slip off the footrests, risking serious injuries such as lacerations and fractures to the feet and ankles. Caregivers frequently struggle to redirect affected residents, as cognitive decline makes it difficult for them to follow verbal instructions.

Our team designed and prototyped a mechanical footrest attachment compatible with standard manual wheelchairs. Rather than restraining the feet, the device uses rollers that allow users to safely express their walking reflex through a natural, gait-like motion. This keeps feet elevated and away from harm while providing gentle, continuous passive exercise. Research has shown that regular low-intensity leg movement of this kind supports vascular health and helps regulate the sleep-wake cycle in dementia patients.

Team Members: Catrina Callow, Karina Kumar, Isabella Ouellette, Nicola Smith

Client: Nicole Lavoie, Long-term Care Secretary, Vancouver Coastal Health

Team 22 – Redesigning a Safer, More Mobile, & Customizable Finger Traction System for Wrist Fracture Reduction

Project Description:

Distal radius fractures (DRFs) is a common orthopedic injury across all demographics, often requiring reduction of the wrist prior to casting to ensure proper bone alignment. Despite the prevalence, current methods for wrist reduction are not standardized, and are generally unwieldy, time-consuming, and raise safety concerns.

Our team has developed a standalone, adjustable traction system designed to support patients recovering from DRFs directly at their hospital bedside. Traditionally, applying tension to the wrist can be a complex and unsafe process; Our device simplifies this using a sturdy, crane-like aluminum frame with an integrated traction system that gently lifts the arm vertically and applies sustained tension via comfortable, automatically-tightening finger traps.

Built for safety and efficiency, the system features a specialized winch and digital scale that allows healthcare providers to set the exact amount of tension required for a patient’s specific needs, with an automatic locking mechanism preventing sudden releases. The design is both stable and highly mobile, featuring wheel attachments for easy maneuverability throughout a busy hospital setting.

By combining precise mechanical control with a focus on patient comfort, we have created a novel device that makes DRF treatment safer, more accurate, and easier to manage for clinicians.

Team Members: Eric Song, Faiza Rahman, Gregg Neelin, Ryan Lo

Client: Dr. Amir Behboudi, Emergency Physician, Peace Arch Hospital

Team 23 – Miniaturized IMU-GPS Sensor

Project Description:

The Human Motion Biomechanics Lab (HuMBL) at UBC is working on a study to address the challenge of preventing ACL injuries in soccer players, particularly females, by improving the accuracy of movement classification and injury pattern analysis using inertial measurement unit (IMU) data. Data collection involves the UBC Women’s Soccer Team wearing IMU sensors over the full duration of their practices and games. In this project, our team designed a miniaturized wearable sensor that integrates a GPS and real-time-clock with HuMBL’s IMU sensor to streamline data labeling and track players’ location on the field, optimizing their data for analysis. This sensor captures data with high precision, has rechargeable battery with a long battery life, and uses an SD card to store data in real time.

Team Members: Amr Sadek, Madison Lang, Qais Alsharif, & Naomi Endale

Client: Dr. Calvin Kuo & Fraser Douglas, Assistant Professor & PhD Candidate, Human Motion Biomechanics Laboratory (HuMBL)

Team 24 – Enhancing Physiotherapy with a Wearable Center of Mass Feedback Device

Project Description:

In Canada, approximately 17,000 ACL reconstruction surgeries are performed each year, yet fewer than 55% of patients successfully return to competitive sport. Effective rehabilitation depends on retraining proper trunk control and center of mass (COM) positioning, but physiotherapists currently lack affordable, quantitative tools to assess these movement patterns in real time.

COMET (Center of Mass Estimation Tool) Version 2 builds on a wearable device originally developed to estimate athletes’ positions during dynamic rehabilitation exercises. Worn on the chest, it monitors trunk height and angle in real time and delivers haptic and audio feedback when movement falls outside a safe range, helping patients correct their technique and reduce the risk of reinjury. This year, the team miniaturized the electronics to reduce device volume by over 50%, engineered a more secure attachment system to reduce signal noise, and improved Bluetooth connectivity for reliable real-time data streaming to the physiotherapist.

Performance was validated through controlled lab testing and real patient rehabilitation sessions, giving clinicians a concrete, objective window into patient progress. Future work includes refining the PCB design, optimizing the user interface, and expanding clinical data collection to further validate the device.

Team Members: Ceana Macatula, Ben Magyar, Amelie Marshall, and Ellie McGregor

Client: Dr. Pawel Kudzia and Diego Grossling, Core Motion