ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2018
Model-Based Dynamic Control Allocation in a Hybrid Neuroprosthesis.
Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04453943 (Modeling, Optimization, and Control Methods for a Personalized Hybrid Walking Exoskeleton), which is not on this map. Cited by 16 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Modeling, Optimization, and Control Methods for a Personalized Hybrid Walking Exoskeleton
Who cites it
16 citing papers in PubMed, 77 citations in OpenAlex.
- Toward a hybrid exoskeleton for crouch gait in children with cerebral palsy: neuromuscular electrical stimulation for improved knee extension.Journal of neuroengineering and rehabilitation · 2020Trial
- Automatic Assist Level Adjustment Function of a Gait Exercise Rehabilitation Robot with Functional Electrical Stimulation for Spinal Cord Injury: Insights from Clinical Trials.Biomimetics (Basel, Switzerland) · 2024Article
- Hybrid FES-exoskeleton control: Using MPC to distribute actuation for elbow and wrist movements.Frontiers in neurorobotics · 2023Article
- Robust Control of the Human Trunk Posture Using Functional Neuromuscular Stimulation: A Simulation Study.Journal of biomechanical engineering · 2022Article
- An Iterative Learning Controller for a Switched Cooperative Allocation Strategy during Sit-to-Stand Tasks with a Hybrid Exoskeleton.IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society · 2022Article
- Biologically Inspired Optimal Terminal Iterative Learning Control for the Swing Phase of Gait in a Hybrid Neuroprosthesis: A Modeling Study.Bioengineering (Basel, Switzerland) · 2022Article
- A deep learning method to predict ankle joint moment during walking at different speeds with ultrasound imaging: A framework for assistive devices control.Wearable technologies · 2022Article
- Development of a Gait Rehabilitation Robot Using an Exoskeleton and Functional Electrical Stimulation: Validation in a Pseudo-paraplegic Model.Progress in rehabilitation medicine · 2022Article
- A Tube-based Model Predictive Control Method to Regulate a Knee Joint with Functional Electrical Stimulation and Electric Motor Assist.IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society · 2021Article
- Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation Using Iterative Learning.Frontiers in robotics and AI · 2021Article
- Using Person-Specific Muscle Fatigue Characteristics to Optimally Allocate Control in a Hybrid Exoskeleton - Preliminary Results.IEEE transactions on medical robotics and bionics · 2020Article
- Model Predictive Control of a Feedback-Linearized Hybrid Neuroprosthetic System With a Barrier Penalty.Journal of computational and nonlinear dynamics · 2019Article
- Sub-optimally Solving Actuator Redundancy in a Hybrid Neuroprosthetic System with a Multi-layer Neural Network Structure.International journal of intelligent robotics and applications · 2019Article
- A Hybrid-FES Based Control System for Knee Joint Movement Control.Basic and clinical neuroscienceArticle
- Maintaining upright posture during perturbed standing in a motor-assisted hybrid neuroprosthesis with powered ankle joints: A feasibility and proof-of-concept study.Journal of rehabilitation and assistive technologies engineeringArticle
- Combining neural stimulation and a powered exoskeletal knee to enhance walking after stroke.Journal of rehabilitation and assistive technologies engineeringArticle
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
A hybrid neuroprosthesis that combines human muscle power, elicited through functional electrical stimulation (FES), with a powered orthosis may be advantageous over a sole FES or a powered exoskeleton-based rehabilitation system. The hybrid system can conceivably overcome torque reduction due to FES-induced muscle fatigue by complementarily using torque from the powered exoskeleton. The second advantage of the hybrid system is that the use of human muscle power can supplement the powered exoskeleton's power (motor torque) requirements; thus, potentially reducing the size and weight of a walking restoration system. To realize these advantages, however, it is unknown how to concurrently optimize desired control performance and allocation of control inputs between FES and electric motor. In this paper, a model predictive control-based dynamic control allocation (DCA) is used to allocate control between FES and the electric motor that simultaneously maintain a desired knee angle. The experimental results, depicting the performance of the DCA method while the muscle fatigues, are presented for an able-bodied participant and a participant with spinal cord injury. The experimental results showed that the motor torque recruited by the hybrid system was less than that recruited by the motor-only system, the algorithm can be easily used to allocate more control input to the electric motor as the muscle fatigues, and the muscle fatigue induced by the hybrid system was found to be less than the fatigue induced by sole FES. These results validate the aforementioned advantages of the hybrid system; thus implying the hybrid technology's potential use in walking rehabilitation.
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Registered trials
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.