Projects
Current Projects (Recruiting new students!):
Wearable Sensor-Based Biomechanical Analysis and Educational Lessons in SLO County Elementary and Middle Schools (Advisor: Dr. Britta Berg-Johansen)
Developing Wearable Devices to Measure Spine Posture (Advisor: Dr. Britta Berg-Johansen)
We are developing a wearable device worn on the lower back that uses sensors to measure forward bending, lateral bending, axial rotation, and combined loading postures. After lab verification, we plan to have farmworking women (via Cal Poly's Mobile Health Unit) wear the device their workdays to monitor harmful postures.
Biomechanics Across Sports and Athletic Activities (Advisor: Dr. Scott Hazelwood)
Current projects with Dr. Hazelwood include running biomechanics, biomechanics of basketball shot form, baseball swing and pitching biomechanics, biomechanics of soccer movements, biomechanics of ankle braces. Dr. Hazelwood is open to biomechanical analyses of various athletic or dynamic movements.
Assessing Sex Differences in Dynamic Movements Related to ACL Injury (Advisor: Dr. Soph Ziemian)
The objective of this project is to characterize sex differences in ACL-related movement mechanics across local youth and college athletes to better understand the factors contributing to higher ACL injury rates in female athletes. While working with youth sport teams, we also plan to engage youth athletes in biomechanics outreach to promote interest in STEM.
Investigating the Impact of Plated Supershoes on Running Form and Injury Risk (Advisor: Dr. Soph Ziemian)
The goal of this study is to investigate how carbon-plated running shoes, commonly known as “supershoes,” influence running biomechanics. While these shoes have been shown to improve running economy by reducing the energy required to run, we are particularly interested in understanding how changes in running mechanics may influence injury risk and patterns over time.
Validating Cell Phone-Based Motion Capture Methods (Advisor: Dr. Soph Ziemian)
This project aims to validate emerging mobile methods for assessing movement mechanics against gold-standard motion capture methods. We are currently collaborating with Valor Vision to help validate their mobile app designed for biomechanics assessments in athletic settings.
Leveraging smartphones and Machine Learning Models for Accessible Balance Assessment (Advisor: Dr. Eric Ocegueda)
Balance assessment is important for evaluating fall risk, monitoring the effects of medication in people with Parkinson’s disease, and tracking recovery after surgery. Smartphones built in sensors may provide an accessible, low-cost alternative to assess balance outside of specialized laboratories. In this project, students will use phone sensor data and biomechanical modeling to estimate balance measures during standing trials, validate the estimates against force-plate data, and investigate whether physics informed machine-learning models can improve prediction accuracy.
Clinical Validation of iPhone Balance App (Advisors: Dr. Steve Klisch and Dr. Britta Berg-Johansen)
We are recruiting two students to work on a project that involves validating our iPhone balance app with data from standard clinical balance tests, such as the Berg Balance Score test. One student would be responsible for collaborating with local physical therapists to test balance biomechanics for their patients. The other student would be responsible for using Apple’s Xcode and chatGPT’s codeX to code, troubleshoot, and implement updates to our Balance app. These students will also work with the larger MBL student team on other balance-related projects.
Past Projects:
Measuring Gait of TKA Patients Pre-op and Post-op Using a Smartphone
In this study, we conducted gait trials for patients in a local orthopedic surgery clinic before and after total knee arthroplasty (TKA) surgery using the OneStep smartphone app. We have validated various gait parameters in the app against motion capture technology, and are measuring differences in gait parameters between pre-operative, 2-weeks post-operative, and 6-weeks post-operative appointments.
Youth Pitching Mechanics
Motion Analysis and Inverse Dynamics of Knee Joint Biomechanics
Reducing Errors Due to Soft Tissue Artifacts (STA)
Reducing Errors Due to Crosstalk
Crosstalk arises due to errors in defining the knee flexion-extension axis. Briefly, errors in locating the flexion-extension axis via misaligned markers on the knee may lead to substantial errors in the measurement of knee varus-valgus angles. This results in a strong, anatomically inaccurate correlation between flexion-extension and varus-valgus motions of the knee, which is considered the “hallmark” of crosstalk in motion analysis studies.
A statistical technique employing linear algebra called Principal Component Analysis (PCA) has been implemented to reduce crosstalk errors. PCA is implemented as a post-hoc correction technique on knee angles that results in a new set of corrected, uncorrelated knee angles.
Kaila Lawson, Harsh Goel and Jordan Skaro developed custom MATLAB codes and protocols for using PCA to correct for crosstalk errors in gait, cycling, and elliptical training exercises.