Levi Hargrove

About

Levi Hargrove is a Professor of Physical Medicine and Rehabilitation and a Professor of Biomedical Engineering (by courtesy) at Northwestern University. His research interests include signal processing, pattern recognition, and myoelectric control of powered prostheses. Dr. Hargrove focuses on the research and development of clinically realizable myoelectric control systems with the goal of making these systems available to amputees in the near-term. His work involves developing innovative control systems for prosthetic devices, aiming to improve functionality and integration for users. He holds a BScE in Electrical Engineering, an MScE in Electrical Engineering, and a PhD in Electrical Engineering from the University of New Brunswick, completed in 2003, 2005, and 2008 respectively.

Research topics

• Computer Science
• Artificial Intelligence
• Medicine
• Simulation
• Physical medicine and rehabilitation
• Embedded system
• Psychology
• Biomedical engineering
• Human–computer interaction
• Mathematics

Selected publications

• Toward higher-performance bionic limbs for wider clinical use

  Nature Biomedical Engineering · 2021 · 261 citations

  PublisherOA PDFDOI

• Design and clinical implementation of an open-source bionic leg

  Nature Biomedical Engineering · 2020 · 227 citations

  In individuals with lower-limb amputations, robotic prostheses can increase walking speed, and reduce energy use, the incidence of falls and the development of secondary complications. However, safe and reliable prosthetic-limb control strategies for robust ambulation in real-world settings remain out of reach, partly because control strategies have been tested with different robotic hardware in constrained laboratory settings. Here, we report the design and clinical implementation of an integra…

  DOI

• Intent Prediction Based on Biomechanical Coordination of EMG and Vision-Filtered Gaze for End-Point Control of an Arm Prosthesis

  IEEE Transactions on Neural Systems and Rehabilitation Engineering · 2020 · 48 citations

  We propose a novel controller for powered prosthetic arms, where fused EMG and gaze data predict the desired end-point for a full arm prosthesis, which could drive the forward motion of individual joints. We recorded EMG, gaze, and motion-tracking during pick-and-place trials with 7 able-bodied subjects. Subjects positioned an object above a random target on a virtual interface, each completing around 600 trials. On average across all trials and subjects gaze preceded EMG and followed a repeatab…

  DOI

• (Un)supervised (Co)adaptation via Incremental Learning for Myoelectric Control: Motivation, Review, and Future Directions

  IEEE Transactions on Neural Systems and Rehabilitation Engineering · 2025-01-01 · 4 citations

  articleOpen access

  This paper presents a narrative review of incremental learning methods for myoelectric control, outlining both the historical trajectory and potential of adaptive prosthetic systems. Traditional myoelectric control has evolved from direct control techniques to advanced pattern recognition, yet persistent challenges such as signal non-stationarities and, consequently, the need for frequent recalibration remain. Incremental learning may enable a paradigm shift by continuously updating control mode…

  PublisherDOI

• Deep-Learning Control of Lower-Limb Exoskeletons via Simplified Therapist Input

  2025-05-12 · 3 citations

  article

  Partial-assistance exoskeletons hold significant potential for gait rehabilitation by promoting active participation during (re)learning of "normal" walking patterns. Typically, the control of interaction torques in partial-assistance exoskeletons relies on a hierarchical control structure. These approaches require extensive calibration due to the complexity of the controller and user-specific parameter tuning, especially for activities like stair or ramp navigation. To address the limitations o…

  PublisherDOI

Recent grants

• Improved function performance in individuals with transradial limb difference using cloud based health system and deep learning

  NIH · $5.3M · 2014–2028

• NRI: Small: Modeling, Quantification, and Optimization of Prosthesis-User Interface

  NSF · $1000k · 2013–2018

• The Functional Importance of Powered Wrist Flexion/Extension and Simultaneous Control for Upper Limb Prostheses

  NIH · $3.0M · 2018–2023

Frequent coauthors

• Todd Kuiken

  177 shared

• Ann M. Simon

  Shirley Ryan AbilityLab

  168 shared

• Eric J. Perreault

  Shirley Ryan AbilityLab

  70 shared

• Aaron J. Young

  Georgia Institute of Technology

  53 shared

• José L. Pons

  Shirley Ryan AbilityLab

  50 shared

• Yue Wen

  Beijing Institute of Technology

  43 shared

• Emek Barış Küçüktabak

  Shirley Ryan AbilityLab

  42 shared

• Matthew R. Short

  41 shared

Similar researchers at Northwestern University

• Tobin J. Marksh-176
• Joseph Hupph-171
• James D Thomash-160