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The focus of the seminar series is to call for experts related to academia and research in the areas related to biomechanics, variability, motor disorders, physical therapy, and related studies.

This week's Seminar features presentations from students on Dr. Aaron Likens research team. 

 

Presenter: Alli Grunkemeyer

Presentation Title: “From Subtle to Significant: Enhancing Heaviness Perception with Stochastic Resonance”

Presentation Abstract: This presentation explores how weak vibrations on the skin can enhance dynamic touch through stochastic resonance (SR), a phenomenon where subtle, noisy vibrations effectively reduce sensory thresholds and enhance haptic perception. Improving sensory feedback is important, especially for aging adults, as it can help them better perceive physical properties of everyday objects.

 

Presenter: Vasileios Mylonas

Presentation Title: “Learning to control a video game with the torso facilitates the transfer of learning to a center-of-pressure-controlled game”

Presentation Abstract: Motion-controlled video games are used in rehabilitation, but transferring skills to other movements is limited. This study developed games involving the center of pressure and trunk rotations to explore motor learning transfer. Results showed better performance and lower entropy in CoP after trunk game training, indicating directional transfer of learning.

 

Presenter: Narges Shakerian

Presentation Title: “Fractal Characteristics of Driving Behavior in Young Adults”

Presentation Abstract: Driving is a complex human behavior that plays a key role in activity of daily living. In young adults, the ability to maintain consistent control is influenced by various driving conditions. We examine the fractal nature of driving behavior, particularly how it adapts to direction, speed, and steering task difficulty. 

 

Presenter: Seung Kyeom Kim

Presentation Title: “Mathematical Approaches to Human Movement”

Presentation Abstract: Would you believe that massive amounts of gait data can be predicted from a single gait cycle? I will show how simple, powerful mathematical models can accurately predict long walking time series. The heart of this approach is symbolic regression, a technique for discovering equations of human motion from data.
 

 

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