Jogging Stroller Mechanics

Running while pushing a jogging stroller is common, yet the biomechanical demands of stroller running remain poorly understood. Our research program examines how stroller use changes running mechanics, loading, muscle activity, and movement strategies using a combination of laboratory biomechanics, wearable sensors, and field-based data collection.

Our published work in PLOS ONE demonstrated that stroller running produces a biomechanical tradeoff: several measures of vertical impact loading were reduced while torsional loading increased. Rather than simply increasing or decreasing running loads, pushing a stroller appears to change how those loads are distributed.

We have extended this work through conference presentations examining ground reaction forces, free moment, 3D joint kinematics, tibial acceleration, muscle activation, arm swing and trunk rotation, hands-free stroller running, stride characteristics, running volume and injury risk, and stroller running on hills. This growing body of work allows us to examine stroller running across multiple biomechanical systems rather than through a single outcome measure.

Current studies are expanding the project beyond traditional laboratory conditions. We are collecting outdoor data to determine how hills and changes in grade affect stroller-running mechanics and loading. A second laboratory study is comparing different stroller types and examining how restrictions on arm swing alter running biomechanics. Together, these studies are helping us understand how the runner, stroller, environment, and running technique interact.

This research also serves as a platform for undergraduate researchers. Students can participate in experimental design, human-subject data collection, motion analysis, wearable sensing, signal processing, programming, statistical analysis, and scientific communication. Individual student projects can address focused questions while contributing to a larger research program, creating opportunities for conference presentations, publications, and continued collaborative work.

Our long-term goal is to develop a comprehensive understanding of stroller-running biomechanics across runners, equipment, techniques, and environments while building a sustainable interdisciplinary research program that supports undergraduate research and collaboration. The project provides a growing foundation for work spanning biomechanics, engineering, rehabilitation, wearable technology, human movement science, and product design.

See our peer-reviewed publication in PLoS One 

• https://doi.org/10.1371/journal.pone.0332616 

See our conference presentations

• Running Volume and Stroller Use: Their Combined Effect on Injury Risk

• Stroller Running Slightly Attenuates the Stride Length–Speed Relationship

• How Does Running with a Jogging Stroller Affect Ground Reaction Force?

• Muscle Activation During Stroller Running

• Free Moment Increases While Running with a Stroller

• Does a Running Stroller Affect 3D Joint Kinematics?

• How Does a Running Stroller Affect Tibial Acceleration During a Run? 

• How Does Hands-Free Stroller Running Affect the Ground Reaction Force?

• Arm Swing, Trunk Rotation, and Free Moment During Running

• Stroller Running on Hills: How Terrain Affects Ground Reaction Forces

and our articles in the Washington Post and The Conversation 

• What running with a jogging stroller does to your body

• Running with a stroller: 2 biomechanics researchers on how it affects your form − and risk of injury 

Our stroller running work has also been featured in the Newsweek article

• Surprising Effect of Running With a Stroller Revealed.

Schmear: Automated OpenCap Gait Analysis Pipeline

Markerless motion capture systems such as OpenCap have dramatically lowered the barrier to collecting biomechanical data. Standard video cameras can now generate detailed estimates of human movement without the cost and infrastructure of a traditional motion capture laboratory. However, collecting the data is only part of the challenge. Turning OpenCap outputs into interpretable gait measures still requires substantial processing, technical expertise, quality control, and manual interpretation.

Schmear was developed to help bridge that gap. The pipeline was originally created in MATLAB to process OpenCap gait data, identify gait events, organize biomechanical measures, and automate analysis and reporting. We are now rebuilding the pipeline in Python with a Jupyter-based interface to improve accessibility, portability, reproducibility, and opportunities for continued development.

The developing workflow is designed to support multiple users. A streamlined clinical pathway produces concise PDF reports that summarize key gait measures, while a research pathway provides more detailed outputs for quality control, statistical analysis, and further investigation. An important part of the redevelopment is preserving the analysis developed in MATLAB while creating a more flexible framework for adding new measures and methods.

Schmear also provides a platform for undergraduate research at the intersection of biomechanics, computing, and data science. Student projects can contribute to gait-event detection, signal processing, software validation, data visualization, quality-control methods, and the development of new biomechanical measures. The modular nature of the pipeline also creates opportunities for collaboration with biomechanics researchers, clinicians, rehabilitation specialists, and others interested in making quantitative gait analysis more accessible.

Our longer-term goal is to develop Schmear into a robust research framework that helps translate increasingly accessible markerless motion capture data into useful and reproducible biomechanical information. This work was presented at the World Congress of Biomechanics:

Bridging OpenCap Outputs to Clinically Useful Gait Reports

Our research group works with undergraduate and graduate students on projects in biomechanics, gait analysis, wearable sensing, markerless motion capture, machine learning, and computational methods for studying human movement.

Student Opportunities

Most of our student research takes place during the summer in a collaborative environment that brings together students from different majors, institutions, academic interests, and stages of their education. Students from early undergraduate years through graduate study work alongside one another and with faculty, contribute to shared projects, and also develop focused questions of their own.

Students learn how to move a research project from an idea to a finished product. This includes study design, literature review, human-subject data collection methods, Vicon post-processing, data analysis, and scientific communication. Students also gain experience presenting research posters and contributing to the writing of conference abstracts, reports, and peer-reviewed papers.

One of our longer-term goals is to grow externally funded summer research opportunities that bring together students from multiple institutions and disciplines to work on connected research problems.

Collaborations

We are interested in collaborations that can help expand this work, including partnerships with researchers in biomechanics, rehabilitation, human movement science, wearable technology, and accessible measurement methods.

We are also interested in working with stroller manufacturers and product-design teams to better understand how stroller design affects running biomechanics and user experience, and to develop research questions that are useful both scientifically and in product development.

Students, faculty, and potential research or industry collaborators who are interested in this work are welcome to reach out.

Footstrike Pattern Identification

Footstrike pattern is commonly classified as rearfoot, midfoot, or forefoot and can influence running mechanics and loading. Traditional classification often relies on visual assessment or laboratory measurements, which can be time-consuming, subjective, and difficult to apply consistently across large datasets.

Our work developed machine-learning approaches to automate footstrike pattern identification from biomechanical data. This research demonstrated that quantitative features extracted from running data can be used to classify footstrike pattern reliably, providing a more objective and scalable alternative to manual assessment.

The work has resulted in peer-reviewed publication in the Journal of Biomechanics and has provided a foundation for continued research in automated gait analysis, wearable sensing, and computational biomechanics. It also contributes directly to our broader effort to develop analysis methods that can move beyond traditional laboratory workflows.

This research creates opportunities for undergraduate students to work with biomechanical datasets, machine learning, signal processing, model validation, and scientific communication. It also provides a natural point of collaboration with researchers interested in running biomechanics, wearable sensors, injury-related loading, and data-driven approaches to human movement.

Our next steps are focused on extending these methods to larger and more varied datasets, evaluating how classification approaches perform across different runners and conditions, and integrating automated gait classification into broader analysis pipelines such as Schmear.

See our peer-reviewed article in the Journal of Biomechanics

Footstrike Pattern Recognition Using Machine Learning on Tibial Accelerometry

Treadmill Retrofit

Instrumented treadmills can provide heel-strike and toe-off timing, ground reaction forces, and center of pressure measurements, but commercial systems can cost well over $100,000.

This project retrofitted a conventional treadmill with load cells to create a lower-cost platform for biomechanics research. A custom PCB interfaced the load cells with a Vicon data acquisition system, and software was developed to convert the measured signals into force and center of pressure estimates during post-processing.

The project demonstrated a lower-cost approach to adding quantitative force measurements to an existing treadmill without requiring a fully instrumented commercial system.

Immersive Virtual Reality System

This project combines the motion tracking and balance board to create an immersive virtual reality environment for one user. Real-time visual information will be relayed via an Oculus Rift. The user will be able to “walk” continuously in a plane.

Virtual Shuffleboard and Air Hockey 

Analysis of the performance of repeated, skilled tasks can elicit information about the underlying motor control system. The stability measurement of the control system may correlate with the health of the user. These measurements may be tracked over time to monitor degradation due to a degenerative illness or monitor improvement after an intervention. Within a session, we may be able to detect when learning ends and when fatigue begins to set in.

Shuffleboard Mark I

Shuffleboard Mark II

Early Prototype Version (Summer 2016): Video courtesy MA Bianco

Thesis-Ready Mark I (Spring 2018)

More technical information is available from the ASB 2018 Regional conference and "A low-cost, open-source virtual air hockey table for human motion applications"

Mahoney Dissertation - https://etda.libraries.psu.edu/catalog/19584

Bianco Thesis - https://honors.libraries.psu.edu/catalog/5326mab6756

Step Counting and Evaluation

Activity tracking has become a popular interest among the health-conscious. However, current consumer products are often inaccurate, especially at low and high speeds, and may not differentiate between different movement activities. Here, we began by developing simple open source algorithms with low-cost IMUs to count steps and validate their accuracy. Next, the quality of the step was investigated: was the step during a walk or a run? We are currently expanding on this detailed investigation. Now, we want to see if a running step hit with the forefoot or rearfoot using a light, wearable device.

Associated Peer-Reviewed Articles:

• Methodology and validation for identifying gait type using machine learning on IMU data

• A comprehensive comparison of simple step counting techniques using wrist- and ankle-mounted accelerometer and gyroscope signals

• Design and validation of a simple automated optical step counting method for treadmill walking

• Knee Angle Estimation with Dynamic Calibration Using Inertial Measurement Units for Running

Inexpensive and Portable 2D Motion Capture

Tracking of lower-body joint angles during walking shows the range of motion during a gait cycle. Applying markers to limbs and using motion analysis techniques automates the process of identifying the limbs and their angle in a plane (or space). Most mainstream commercially-available systems cost over $100,000 for the camera and software system. For this project, we developed a low cost (<$1000 without MATLAB) system for 2D motion tracking. This was employed in conjunction with a project to track the range of motion of the ankle and knee when a subject is wearing an Ankle-Foot Orthosis (AFO). The project is being developed to work in real time and extended into 3D motion tracking using multiple cameras.

More technical information available from the ASB 2015 conference. The detection algorithm was adapted for the aforementioned step counting work.

Daily Fantasy Football Roster Optimization

Daily Fantasy Football has become an increasingly popular activity in the last several years. The problem translates into a Stochastic Knapsack Problem: attempting to pack as much value as possible into a constrained environment. The added difficulty is that the value of each “item” is uncertain before the sack is packed. Using machine learning and linear programming, we attempt to create lineups that outperform random and real-world competitors.

Method and Validation for Optimal Lineup Creation for Daily Fantasy Football Using Machine Learning and Linear Programming

Continuous Vital Sign Monitor

Heart disease is the leading cause of death in the United States. Early detection of disease and monitoring of disease progression could prevent or delay many of these deaths. Analysis of the heartbeat rhythm has been shown to be predictive or indicative of certain cardiovascular diseases. The ability to constantly monitor a person’s heartbeat and alert the user and their doctor of a possible problem could lead to earlier disease detection. Simultaneously monitoring other vital signs (e.g., temperature and blood pressure) can provide more information about the state of health of the individual.

Balance Board for Quiet Stance Testing 

Tracking and analyzing the center of pressure (COP) of a subject can indicate and track neurodegenerative disorders. Precision balance boards can cost over $10,000. These often measure 6 degrees of freedom, but for COP tracking, 1DOF is enough. This project designed, calibrated and validated a 1DOF balance board for under $500 (without MATLAB). More technical information is available from the ASB 2016 conference.

Instrumented Bicycle

In this project, we attached sensors to a bicycle to track its position, speed and torque in real time. This information can be used to control an electronic-assist motor and log the activity of the user.

Research Media and Features

Selected media coverage and institutional features highlighting the impact of our biomechanics research, undergraduate research mentorship, and efforts to make human-movement analysis more accessible. See News for broader updates on engineering education and the John R. Post School of Engineering.