Jogging Stroller Mechanics

Running remains a popular physical activity due to its cardiovascular and musculoskeletal benefits. For parents with young children, “jogging strollers” have made it easier to balance fitness with caregiving. These strollers, featuring a fixed front wheel and two rear wheels, have seen significant design improvements over the past two decades.

Research indicates that performance metrics such as running economy and perceived exertion are impacted when running with a stroller. However, prior studies have largely focused on kinematic measures, leaving gaps in understanding ground reaction forces (GRF) during stroller running. Our research aims to address this gap by analyzing both the kinetics and kinematics of stroller running to better understand potential links to running-related injuries (RRI).

Over the past several years, we have conducted data collection in our gait lab and in outdoor settings, including varied terrains such as flat and inclined surfaces. This work aims to provide insights into the biomechanical adaptations required for stroller running and their implications for injury prevention.

See our conference presentions:

• 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 article 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.

Footstrike Pattern Identification

Running-related overuse injuries are influenced by multiple factors, with high-impact loading playing a prominent role. High-impact loading, often measured by vertical loading rates, is significantly affected by how the foot strikes the ground. Most runners adopt a rearfoot strike (RFS) pattern, where the heel contacts the ground first. However, evidence suggests that RFS runners experience injuries more frequently compared to those who utilize midfoot (MFS) or forefoot (FFS) strike patterns. MFS and FFS are characterized by landing flat-footed or on the ball of the foot, respectively.  

To address the prevalence of overuse injuries, wearable technology may offer a solution by identifying footstrike patterns (FSP) in runners. Modern wearable devices leverage accelerometry to capture biomechanical data, presenting an opportunity to utilize machine learning (ML) for precise FSP classification. This study aims to apply ML techniques to tibial accelerometry data to accurately identify FSP. Building on the proven efficacy of ML in gait classification research, it is hypothesized that this approach can reliably distinguish FSP using acceleration data alone, providing valuable insights for injury prevention and runner training.  

See our paper in Journal of Biomechanics 

Footstrike Pattern Recognition Using Machine Learning on Tibial Accelerometry

Treadmill Retrofit

The ability to track the time of heelstrikes and toe-offs as well as the location of the center of pressure (CoP) of a user on a treadmill would be beneficial for Biomechanics research. However, turn-key instrumented treadmills can cost over $100,000. The project retrofits a treadmill with loadcells to measure the force on each leg. A PCB was designed to interface the load cells to a Vicon data acquisition box. Software was developed to convert the captured voltages from the four legs into the CoP location in post-processing.

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.

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 Papers:

• 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

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"

Inexpensive & 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 available from the ASB 2016 conference and a forthcoming validation paper.

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 and electronic assist motor and log the activity of the user.

Media

• Scholar, professor looking to ‘black box’ the human body

• Not Just Noise

• Students design workstations that accommodate groups and individuals

• Increased arm swing asymmetry is early sign of Parkinson’s disease