ArticleThe Journal of international medical research2017
Artificial neural network EMG classifier for functional hand grasp movements prediction.
Article in The Journal of international medical research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 72 citations in OpenAlex.
- Upper limb soft robotic wearable devices: a systematic review.Journal of neuroengineering and rehabilitation · 2022Pooled it
- Insights into motor impairment assessment using myographic signals with artificial intelligence: a scoping review.Biomedical engineering letters · 2025Review
- Multimodal Deep Learning Model for Cylindrical Grasp Prediction Using Surface Electromyography and Contextual Data During Reaching.Biomimetics (Basel, Switzerland) · 2025Article
- Discrimination of finger movements by magnetomyography with optically pumped magnetometers.Scientific reports · 2023Article
- Empowering Hand Rehabilitation with AI-Powered Gesture Recognition: A Study of an sEMG-Based System.Bioengineering (Basel, Switzerland) · 2023Article
- Transfer learning in hand movement intention detection based on surface electromyography signals.Frontiers in neuroscience · 2022Article
- The classification of movement intention through machine learning models: the identification of significant time-domain EMG features.PeerJ. Computer science · 2021Article
- An efficient approach for physical actions classification using surface EMG signals.Health information science and systems · 2020Article
- Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography.Sensors (Basel, Switzerland) · 2020Article
- Feasibility and safety of shared EEG/EOG and vision-guided autonomous whole-arm exoskeleton control to perform activities of daily living.Scientific reports · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective To design and implement an electromyography (EMG)-based controller for a hand robotic assistive device, which is able to classify the user's motion intention before the effective kinematic movement execution. Methods Multiple degrees-of-freedom hand grasp movements (i.e. pinching, grasp an object, grasping) were predicted by means of surface EMG signals, recorded from 10 bipolar EMG electrodes arranged in a circular configuration around the forearm 2-3 cm from the elbow. Two cascaded artificial neural networks were then exploited to detect the patient's motion intention from the EMG signal window starting from the electrical activity onset to movement onset (i.e. electromechanical delay). Results The proposed approach was tested on eight healthy control subjects (4 females; age range 25-26 years) and it demonstrated a mean ± SD testing performance of 76% ± 14% for correctly predicting healthy users' motion intention. Two post-stroke patients tested the controller and obtained 79% and 100% of correctly classified movements under testing conditions. Conclusion A task-selection controller was developed to estimate the intended movement from the EMG measured during the electromechanical delay.
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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.