Evidence map›Paper›PMID 37091069›Full record

ArticleFrontiers in neurorobotics2023

Hybrid FES-exoskeleton control: Using MPC to distribute actuation for elbow and wrist movements.

Nathan Dunkelberger, Jeffrey Berning, Eric M Schearer, Marcia K O'Malley

Abstract read
In one paragraph

Article in Frontiers in neurorobotics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Nathan DunkelbergerDepartment of Mechanical Engineering, Mechatronics and Haptics Interfaces Laboratory, Rice University, Houston, TX, United States.
Jeffrey BerningDepartment of Mechanical Engineering, Mechatronics and Haptics Interfaces Laboratory, Rice University, Houston, TX, United States.
Eric M SchearerCenter for Human Machine Systems, Department of Mechanical Engineering, Cleveland State University, Cleveland, OH, United States.
Marcia K O'MalleyDepartment of Mechanical Engineering, Mechatronics and Haptics Interfaces Laboratory, Rice University, Houston, TX, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Individuals who have suffered a cervical spinal cord injury prioritize the recovery of upper limb function for completing activities of daily living. Hybrid FES-exoskeleton systems have the potential to assist this population by providing a portable, powered, and wearable device; however, realization of this combination of technologies has been challenging. In particular, it has been difficult to show generalizability across motions, and to define optimal distribution of actuation, given the complex nature of the combined dynamic system. Methods: In this paper, we present a hybrid controller using a model predictive control (MPC) formulation that combines the actuation of both an exoskeleton and an FES system. The MPC cost function is designed to distribute actuation on a single degree of freedom to favor FES control effort, reducing exoskeleton power consumption, while ensuring smooth movements along different trajectories. Our controller was tested with nine able-bodied participants using FES surface stimulation paired with an upper limb powered exoskeleton. The hybrid controller was compared to an exoskeleton alone controller, and we measured trajectory error and torque while moving the participant through two elbow flexion/extension trajectories, and separately through two wrist flexion/extension trajectories. Results: The MPC-based hybrid controller showed a reduction in sum of squared torques by an average of 48.7 and 57.9% on the elbow flexion/extension and wrist flexion/extension joints respectively, with only small differences in tracking accuracy compared to the exoskeleton alone. Discussion: To realize practical implementation of hybrid FES-exoskeleton systems, the control strategy requires translation to multi-DOF movements, achieving more consistent improvement across participants, and balancing control to more fully leverage the muscles' capabilities.

Indexed as

functional electrical stimulation (FES)hybrid control (HC)model predictive control (MPC)movement assistanceupper limb exoskeleton

Identifiers

PMID37091069
PMCPMC10118008

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.