ArticleJournal of neuroengineering and rehabilitation2019
Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling.
Article in Journal of neuroengineering and rehabilitation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 4 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 4 syntheses or guidelines pooled it, 122 citations in OpenAlex.
- Control strategies used in lower limb exoskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness.Journal of neuroengineering and rehabilitation · 2023Pooled it
- Effects of control strategies on gait in robot-assisted post-stroke lower limb rehabilitation: a systematic review.Journal of neuroengineering and rehabilitation · 2022Pooled it
- Modelling the interaction between wearable assistive devices and digital human models-A systematic review.Frontiers in bioengineering and biotechnology · 2022Pooled it
- Systematic review on wearable lower-limb exoskeletons for gait training in neuromuscular impairments.Journal of neuroengineering and rehabilitation · 2021Pooled it
- A Non-Invasive Simplified Model for Estimating Lower Limb Muscle Forces During Slow Gait in Older Adults and Post-Stroke Individuals.Biomimetics (Basel, Switzerland) · 2026Article
- Myoelectric Control in Rehabilitative and Assistive Soft Exoskeletons: A Comprehensive Review of Trends, Challenges, and Integration with Soft Robotic Devices.Biomimetics (Basel, Switzerland) · 2025Review
- Biomechanical models in the lower-limb exoskeletons development: a review.Journal of neuroengineering and rehabilitation · 2025Review
- Frequency-specific intermuscular coherence of synergistic muscles during an isometric force generation task.Frontiers in neural circuits · 2025Article
- Simultaneous and Proportional Myoelectric Control of Multiple Degrees of Freedom in Individuals With Chronic Hemiparesis.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Intuitive and versatile bionic legs: a perspective on volitional control.Frontiers in neurorobotics · 2024Article
- A Musculoskeletal Model of the Hand and Wrist Capable of Simulating Functional Tasks.IEEE transactions on bio-medical engineering · 2023Article
- Virtual Stiffness: A Novel Biomechanical Approach to Estimate Limb Stiffness of a Multi-Muscle and Multi-Joint System.Sensors (Basel, Switzerland) · 2023Article
- Article
- sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning.Sensors (Basel, Switzerland) · 2022Article
- User-centered design and development of TWIN-Acta: A novel control suite of the TWIN lower limb exoskeleton for the rehabilitation of persons post-stroke.Frontiers in neuroscience · 2022Article
- Multi-Day EMG-Based Knee Joint Torque Estimation Using Hybrid Neuromusculoskeletal Modelling and Convolutional Neural Networks.Frontiers in robotics and AI · 2022Article
- Coordination Between Partial Robotic Exoskeletons and Human Gait: A Comprehensive Review on Control Strategies.Frontiers in bioengineering and biotechnology · 2022Review
- Review of control strategies for lower-limb exoskeletons to assist gait.Journal of neuroengineering and rehabilitation · 2021Review
- Neuromechanics and Energetics of Walking With an Ankle Exoskeleton Using Neuromuscular-Model Based Control: A Parameter Study.Frontiers in bioengineering and biotechnology · 2021Article
- Introduction of a sEMG Sensor System for Autonomous Use by Inexperienced Users.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
Abstract
backgroundResearch efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery.
methodsWe have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time.
resultsWe demonstrated patients' control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients.
conclusionsContinuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots.
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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.