Congratulations to Dr. Connor Olsen

Congratulations to Connor Olsen, who successfully defended his doctoral dissertation in Electrical and Computer Engineering.

Connor’s research asked what can be decoded from the wrist. Traditional myoelectric control records EMG from the forearm to drive bionic limbs. Connor moved the recording site to the wrist and worked on the two problems standing in the way: how the electrode meets the skin, and how we measure whether a myoelectric system is actually working.

That opened applications well past prosthetics. His grant-funded work at the Neilsen Rehabilitation Hospital lets patients control their room’s lights, blinds, and TV through hand gestures alone. He also led research on wrist EMG for gesture classification in stroke patients, and coauthored the lab’s 2025 Nature Communications paper on AI-controlled bionic hands.

Along the way he mentored a long line of undergraduate researchers, and built an onboarding program to get the next ones started faster.

Congratulations, Dr. Olsen. Well earned.

Nature Communications paper hits major global impact milestone

Congratulations to Marshall Trout and co-authors Fredi, Connor Olsen, Taylor Hansen, and David J. Warren on their recent publication in Nature Communications.

The paper has already received significant global visibility, including:

  • Altmetric score: 813 (99th percentile; top 5% of all research outputs tracked)

  • 143 unique news stories, syndicated across 500+ outlets

  • Estimated reach of 406,729 unique viewers

Read the Altmetric report: Click Here
Paper: Shared human-machine control of an intelligent bionic hand improves grasping and decreases cognitive burden for transradial amputees

New Nature Communications paper highlights smarter, more intuitive bionic-hand control

A University of Utah team led by Marshall Trout and Jacob A. George published a new paper in Nature Communications demonstrating shared human–machine control for a commercial bionic hand. By integrating proximity + pressure sensing and using AI to help each finger “find” contact automatically, users can maintain control while the hand handles the fine-grain adjustments—resulting in more secure, more precise grasps with lower cognitive burden.

Read the paper

As of Jan 13, 2026, the paper has an Altmetric attention score of 814.