![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.](https://static.wixstatic.com/media/3f9178_4bd37c9bb5aa405780230437b56ca1f1~mv2.jpg/v1/crop/x_0,y_412,w_3649,h_3966/fill/w_460,h_500,al_c,q_80,usm_0.66_1.00_0.01,enc_auto/0245%20Sasha%20Portnova%20by%20alan%20luntz.jpg)
Alexandra "Sasha"
Portnova
Postdoctoral Fellow at CREATE
Department of Computer Science & Engineering
University of Washington
Email: alexandra.portnova@gmail.com
Bio
Hi!
My name is Sasha and I am passionate about the complexity of human hands. With 27 degrees-of-freedom, controlled by 17 muscles, hands are an intricate biological system, which makes replacing or even improving their function a challenging yet exciting task. Over the course of eight years conducting academic research, I have been working on a multitude of projects to assist or replicate the functionality of a human hand. During my undergraduate years, I worked with Dr. Kat Steele on developing an open-source 3D-printed wrist-driven orthosis for individuals with cervical spinal cord injury. Then, as a PhD student under the supervision of Dr. Sandro Mussa-Ivaldi and Dr. Eric Rombokas, I attempted to simplify the complexity of controlling myoelectric hand prostheses via machine learning algorithms. In my current postdoctoral project, I am working on understanding how inexpensive hand-tracking systems can be utilized for therapeutic exercises to improve hand function in a variety of populations. My ultimate goal is to continue research in an academic setting, focusing on the complexity of human hands in the field of rehab and assistive technologies.
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.