About

Elliott Rouse is an Associate Professor in the Department of Mechanical Engineering at the University of Michigan. His research interests include precision machine design, development of exoskeletons and robotic prostheses, brushless motors, dynamics of human locomotion, human perception and psychophysics, neural control of movement, biomechanics, and human performance augmentation. He is involved in multiple research areas such as biomechanics & biosystems engineering, controls, design, mechatronics, and robotics. Rouse has made significant contributions to the field of robotics and biomedical engineering, notably through the publication of a paper in Nature Biomedical Engineering on the design and clinical implementation of an open-source bionic leg. His work with the Neurobionics Lab has been recognized as one of the most innovative robotics companies of 2020 by Fast Company. He received the Henry Russel Award, the university’s highest honor for early or mid-career faculty members, in 2023. Rouse's research and innovations focus on advancing prosthetic technology and understanding human movement, contributing to the development of rapid prototyping platforms for prosthetics and enhancing human performance through engineering solutions.

Research topics

• Computer Science
• Artificial Intelligence
• Embedded system
• Surgery
• Engineering
• Medicine
• Simulation

Selected publications

• neurobionics/onshape-robotics-toolkit: onshape-robotics-toolkit: v0.5.0

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-16

  otherOpen access

  0.5.0 (2026-01-16) Features add support for composite parts (1b317c1) Bug Fixes add MJCF robot methods to config and update examples (19329ad) revert set variable method and validate variables argument before building payload (25f0883) update variables config to accomodate the set_variables fix (c5d35f4)

  PublisherDOI

• neurobionics/onshape-robotics-toolkit: onshape-robotics-toolkit: v0.6.0

  Zenodo (CERN European Organization for Nuclear Research) · 2026-02-15

  otherOpen access

  0.6.0 (2026-02-15) Features Add mate connector support to CAD parser (891230b) support fetching mate connector (f043f16) Bug Fixes Add MATE_CONNECTOR and MATE_GROUP to FeatureType enum (c3f3136) linting and formatting issues in tests/test_mate_connectors.py (12a554c) Documentation update examples and getting started docs. Remove support for 3.14 to avoid lxml installation problems (4a9d1dd)

  PublisherDOI

• Unsupervised Domain Adaptation for Gait State Estimation

  2025-05-19

  articleSenior author

  Exoskeleton controllers have recently employed machine learning (ML) techniques to provide appropriate assistance throughout the terrains of the real world. One successful approach has been to learn a mapping between an exoskeleton wearer's kinematic measurements and a gait state vector that encodes how the wearer is currently walking (i.e. gait phase, speed), and then dynamically update the assistance based on the gait state. However, these methods require paired datasets of input kinematics to output gait states, which usually involves manual, time-consuming labeling of data from participants wearing specific exoskeletons and thus limits the scalability of these ML methods. A prior solution to this challenge—leveraging large pre-labeled datasets of normative human walking—introduces another problem, in that networks trained on these datasets learn only normative locomotion patterns, and thus may deteriorate when the data are changed by wearing the exoskeleton itself. In this context, we present an unsupervised-learning-based approach to both bypass the requirement of labeled data for gait state prediction and address the difficulty of domain adaptation from normative to exoskeleton-assisted walking. We validate our method in a set of walking simulations that featured exoskeleton data from 14 participants. This model showed significant improvements in state estimation relative to a model trained solely on pre-labeled normative walking, while also not requiring ground truth labels. This work presents a foundation that demonstrates labeled, device-specific data may not be required for predicting walking behavior in real time.

  PublisherDOI

• Myoassist 0.1: Myosuite for Dexterity and Agility in Bionic Humans

  2025-05-12

  article

  Accurate and reliable digital twins of humans and wearable robots can revolutionize rehabilitation robotics. Here, we introduce MyoAssist 0.1, a sub-suite of MyoSuite focused on musculoskeletal simulation environments with assistive devices such as prosthetics and exoskeletons. This open-source platform enables the study and development of human-device interactions, control strategies, and assistive robotics. We present two new simulation environments: myoMPL featuring an arm amputee model with a robotic prosthetic arm, and myoOSL featuring a leg amputee model with a robotic prosthetic leg. The myoMPL environment features a bimanual manipulation task for a shoulder disarticulation amputee using a Modular Prosthetic Limb (MPL), where the task is to pick up an object with the biological hand, pass it to the prosthetic hand, and place it at a target location. The myoOSL environment simulates an above-knee amputee using the Open-Source Leg (OSL) to traverse challenging terrains such as rough surfaces, hills, and stairs. Despite some simplifications in modeling the nuanced constraints of human and prosthetic systems under real-world conditions, these environments provide a foundational simulation framework that supports interdisciplinary research on the interplay between musculoskeletal dynamics and assistive devices. Both myoMPL and myoOSL are featured in MyoChallenge, an annual competition at the NeurIPS conference. All code is accessible through the MyoSuite GitHub repository.

  PublisherDOI

• Accurately Modeling the Output Torque and Stiffness of Ankle-Foot Orthoses with a Compliant Linkage Model

  2025-05-19

  articleSenior author

  The stiffness of passive lower-limb exoskeletons and orthoses governs their assistance. A common practice in the design of these systems is to assume the stiffness of the device is determined only by the intended elastic element (e.g., spring), while the structural components, human attachments, and soft tissues are considered rigid. In practice, the mechanical behavior of orthoses is significantly affected by the compliance of these elements, which drastically impacts the assistance provided. In this work, we present a linkage model with compliant elements that can accurately predict the applied stiffness of ankle-foot orthoses, and retroactively estimate the stiffness of unintended spring elements from published data. The compliant model accurately predicted the torque trajectories of two published passive orthoses with modeled peak torques within 4 % to 7 % of measured values. In contrast, the rigid model greatly overestimated the peak torques, predicting 203 % to 376 % of the measured values. The compliant model also indicated that an onboard joint encoder could only measure 52 % to 69 % of the peak ankle angle recorded with motion capture. The compliant model was also used to reassess the stiffness range of a variable-stiffness orthosis, indicating that its adjustable range is likely 69 % of rigid model predictions. Overall, this work highlights the need to consider how unmodeled compliance affects the mechanical behavior of orthoses and provides a foundation for further exploration.

  PublisherDOI

• An integrated multi-variable optimization approach to tailor ankle-foot orthosis stiffness to end-user needs

  Research Square · 2025-08-21

  preprintOpen access
  PublisherOA PDFDOI

• Decoupling the Feedback and Feedforward Components of Impedance Controllers: Theory and Experiments in Lower-Limb Prostheses

  2025-10-28

  articleOpen access

  Robotic prostheses commonly use joint-space impedance controllers parameterized by stiffness, damping, and an equilibrium angle to create desired behaviors. Although these controllers are often interpreted as equilibrium angle tracking controllers, their parameters are chosen such that ground and user interactions cause a different kinematic pattern to emerge, complicating their design, tuning, and interpretation. To address this challenge, we introduce an alternative formulation of the impedance controller comprising both a feedback position control term and feedforward torque control term. This equivalent form clarifies how the impedance parameters shape both the nominal and perturbed behaviors of the controller. In both theory and experiments with an above-knee amputee participant, we demonstrate that controllers with appropriately designed feedforward torque components can produce identical nominal behaviors despite differences in stiffness and damping, which primarily govern how the system responds to perturbations. Our findings offer important insights for prosthesis controller design and tuning: 1) our decoupled parameterization allows independent prescription of an impedance controller’s nominal and off-nominal behaviors; 2) tuning stiffness and damping based on nominal walking alone is insufficient; and 3) even non-impedance paradigms can benefit from applying impedance concepts to achieve robust real-world behavior.

  PublisherDOI

• Characterization of a Quasi-Direct Drive Knee Perturbation System for Mechanical Impedance Estimation

  IEEE Robotics and Automation Letters · 2025-02-06

  articleSenior author

  The mechanical impedance of the human lower-limb joints during locomotion encodes our understanding of how the neuromotor system regulates the behavior of these tasks. Impedance is also a key component of several strategies for translating this behavior to robots, powered prosthetic limbs, and people empowered by exoskeletons. However, due to difficulty in making accurate measurements, there is little empirical evidence for the impedance behaviors of joints other than the ankle during active walking tasks. In this letter we propose a measurement system based on a highly backdrivable quasi-direct-drive actuator and a carefully calibrated actuator torque model. Bench-top validation with known mechanical impedance human-substitutes, confirms the viability of this system as an impedance measurement tool. A pilot study with two subjects utilizing a custom knee-exoskeleton apparatus confirms the feasibility of this system for human walking experiments.

  PublisherDOI

• Rethinking Energy Storage and Return in Prosthetic Feet: User Preferences Challenge Conventional Wisdom

  2024-09-01

  articleSenior author

  Modern prosthetic feet have spring-like mechanics, deflecting and storing energy during mid-stance, and returning this energy during terminal stance. Researchers and manufacturers of prosthetic feet often tout the high energy storage and return of new prosthetic designs, but there is limited evidence supporting the notion that more energy storage and return is best for the user in terms of either biomechanical outcomes or user preference. In this paper, the relationship between ankle stiffness and energy storage is evaluated at stiffness levels within ± 20% of the user-preferred (self-selected) stiffness. For all eight amputee subjects, energy storage and return was highest at stiffness levels below the preferred stiffness. These results indicate that maximal energy storage and return may occur at an uncomfortably low stiffness, casting doubt on the utility of energy storage and return as a metric for evaluating prosthetic designs.

  PublisherDOI

• A usability study on the inGAIT-VSO: effects of a variable-stiffness ankle-foot orthosis on the walking performance of children with cerebral palsy

  Research Square · 2024-05-10 · 1 citations

  preprintOpen access
  PublisherOA PDFDOI

Recent grants

• CAREER: Reverse Engineering Human Leg Mechanics to Transform Control of Robotic Prostheses

  NSF · $550k · 2019–2025

• Quantification of dynamic ankle mechanics for assessment and treatment of locomotor dysfunction post stroke

  NIH · $181k · 2019–2021

• NIH Grant F31NS074687

  NIH · $33k · 2012

• Quantification of dynamic ankle mechanics for assessment and treatment of locomotor dysfunction post stroke

  NIH · $220k · 2019–2022

• NRI: FND: COLLAB: An Open-Source Robotic Leg Platform that Lowers the Barrier for Advanced Prosthetics Research

  NSF · $342k · 2017–2017

Frequent coauthors

• Levi J. Hargrove

  Northwestern University

  38 shared

• Max K. Shepherd

  Northeastern University

  36 shared

• Nikko Van Crey

  Michigan United

  27 shared

• Todd Kuiken

  26 shared

• Gray C. Thomas

  University of Michigan–Ann Arbor

  22 shared

• Ignacio Martínez-Caballero

  Hospital Infantil Universitario Niño Jesús

  22 shared

• Edwin H. F. van Asseldonk

  National Academies of Sciences, Engineering, and Medicine

  20 shared

• Robert D. Gregg

  University of Michigan–Ann Arbor

  20 shared

Labs

• Neurobionics LabPI

Education

• Postdoctoral Fellow, MIT Media Lab

  Massachusetts Institute of Technology

  2014

• PhD, Biomedical Engineering

  Northwestern University

  2012

Awards & honors

• Henry Russel Award (2023)
• Open Source Bionic Leg paper in Nature Biomedical Engineerin…
• Neurobionics Lab named one of the most innovative robotics c…
• NSF CAREER Award (2019)
• Second place in IEEE Student Paper Competition (2018)