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Headshot of Sasha Portnova, a white female with short brown hair up to her chin. She is wearing a dark blue dress with red, yellow, and green flowers and long gold earrings.

Alexandra "Sasha"
Portnova

Research Scientist Engineer at UW

Department of Mechanical Engineering

University of Washington

Email: alexandra.portnova@gmail.com

Bio

Hi!

My name is Sasha, and I am captivated by the remarkable complexity and significance of human hands. These intricate biological marvels, with 27 degrees of freedom powered by over 30 muscles, are essential for countless everyday tasks — from the delicate precision of threading a needle to the powerful grip of lifting a child. The hand's versatility and elegance make understanding, replicating, or enhancing its function a profound challenge that drives my research.

Over the past decade, I have explored human hands through multiple lenses: rehabilitation, assistive technology, and prosthetics. As an undergraduate researcher with Dr. Kat Steele, I developed an open-source, 3D-printed wrist-driven orthosis to restore function for individuals with cervical spinal cord injury. Later, during my PhD with Drs. Mussa-Ivaldi and Rombokas, I tackled the complexity of myoelectric prosthetic control, using machine learning algorithms to translate neural signals into intuitive hand movements. Then, as a postdoctoral researcher, I investigated how accessible hand-tracking systems could be leveraged for therapeutic exercises, helping people improve their hand function across diverse populations.

My ultimate goal is to continue unraveling the intricacies of human hands through research. I aim to contribute to innovations in human-computer interactions, rehabilitation, and assistive technology to enhance people's lives, promote accessibility, and bridge the gap between biology and engineering.

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Latest Publication

Accuracy of Video-Based Hand Tracking for People With Upper-Body Disabilities

In this paper, we assess the accuracy of a commercially-available hand-tracking device, Leap, to track hands of individuals with and without upper-body disabilities. We also propose the use of Principal Component Analysis (PCA) as a tool to compare high-dimensional biosignals on a lower-dimensional space.

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