ArticleSensors (Basel, Switzerland)2014
An upper-limb power-assist exoskeleton using proportional myoelectric control.
Article in Sensors (Basel, Switzerland), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 3 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Systematic review of AI/ML applications in multi-domain robotic rehabilitation: trends, gaps, and future directions.Journal of neuroengineering and rehabilitation · 2025Pooled it
- Myoelectric Control Systems for Upper Limb Wearable Robotic Exoskeletons and Exosuits-A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- EEG-Based Control for Upper and Lower Limb Exoskeletons and Prostheses: A Systematic Review.Sensors (Basel, Switzerland) · 2018Pooled it
- The Evolution of Machine Learning and Its Applications in Orthopaedics: A Bibliometric Analysis.Cureus · 2025Review
- Insights into motor impairment assessment using myographic signals with artificial intelligence: a scoping review.Biomedical engineering letters · 2025Review
- Upper limb human-exoskeleton system motion state classification based on semg: application of CNN-BiLSTM-attention model.Scientific reports · 2025Article
- Machine Learning- and Deep Learning-Based Myoelectric Control System for Upper Limb Rehabilitation Utilizing EEG and EMG Signals: A Systematic Review.Bioengineering (Basel, Switzerland) · 2025Review
- Passive and Active Exoskeleton Solutions: Sensors, Actuators, Applications, and Recent Trends.Sensors (Basel, Switzerland) · 2024Review
- A Review of Potential Exoskeletons for the Prevention of Work-Related Musculoskeletal Disorders in Agriculture.Sensors (Basel, Switzerland) · 2024Review
- A motor imagery classification model based on hybrid brain-computer interface and multitask learning of electroencephalographic and electromyographic deep features.Frontiers in physiology · 2024Article
- Evaluation of a machine-learning-driven active-passive upper-limb exoskeleton robot: Experimental human-in-the-loop study.Wearable technologies · 2023Article
- Article
- MCR-ALS-based muscle synergy extraction method combined with LSTM neural network for motion intention detection.Frontiers in neurorobotics · 2023Article
- IMU Motion Capture Method with Adaptive Tremor Attenuation in Teleoperation Robot System.Sensors (Basel, Switzerland) · 2022Article
- A Wearable, Multi-Frequency Device to Measure Muscle Activity Combining Simultaneous Electromyography and Electrical Impedance Myography.Sensors (Basel, Switzerland) · 2022Article
- Model-Based Mid-Level Regulation for Assist-As-Needed Hierarchical Control of Wearable Robots: A Computational Study of Human-Robot Adaptation.Robotics (Basel, Switzerland) · 2022Article
- Real-Time Control of a Multi-Degree-of-Freedom Mirror Myoelectric Interface During Functional Task Training.Frontiers in neuroscience · 2022Article
- Biosignal-Based Human-Machine Interfaces for Assistance and Rehabilitation: A Survey.Sensors (Basel, Switzerland) · 2021Review
- Design and Development of an Upper Limb Rehabilitative Robot with Dual Functionality.Micromachines · 2021Article
- Evaluating Convolutional Neural Networks as a Method of EEG-EMG Fusion.Frontiers in neurorobotics · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We developed an upper-limb power-assist exoskeleton actuated by pneumatic muscles. The exoskeleton included two metal links: a nylon joint, four size-adjustable carbon fiber bracers, a potentiometer and two pneumatic muscles. The proportional myoelectric control method was proposed to control the exoskeleton according to the user's motion intention in real time. With the feature extraction procedure and the classification (back-propagation neural network), an electromyogram (EMG)-angle model was constructed to be used for pattern recognition. Six healthy subjects performed elbow flexion-extension movements under four experimental conditions: (1) holding a 1-kg load, wearing the exoskeleton, but with no actuation and for different periods (2-s, 4-s and 8-s periods); (2) holding a 1-kg load, without wearing the exoskeleton, for a fixed period; (3) holding a 1-kg load, wearing the exoskeleton, but with no actuation, for a fixed period; (4) holding a 1-kg load, wearing the exoskeleton under proportional myoelectric control, for a fixed period. The EMG signals of the biceps brachii, the brachioradialis, the triceps brachii and the anconeus and the angle of the elbow were collected. The control scheme's reliability and power-assist effectiveness were evaluated in the experiments. The results indicated that the exoskeleton could be controlled by the user's motion intention in real time and that it was useful for augmenting arm performance with neurological signal control, which could be applied to assist in elbow rehabilitation after neurological injury.
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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.