Artificial neural network EMG classifier for functional hand grasp movements prediction
| Autor: | Giancarlo Ferrigno, Simona Ferrante, Michele Cotti Cottini, Carlo A. Seneci, Alessandra Pedrocchi, Eleonora Guanziroli, Marta Gandolla, Davide Baldassini, Franco Molteni |
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| Rok vydání: | 2016 |
| Předmět: |
Adult
Male 030506 rehabilitation EMG controller Electromyography (EMG) artificial neural networks hand rehabilitation movement prediction Movement Kinematics Electromyography Wrist Biochemistry 03 medical and health sciences 0302 clinical medicine Hand strength Task Performance and Analysis medicine Humans Computer vision Electrodes Special Issue: Stroke research: current advances and future hopes Hand Strength Artificial neural network medicine.diagnostic_test business.industry Biochemistry (medical) Hand grasp GRASP Cell Biology General Medicine Hand Biomechanical Phenomena body regions medicine.anatomical_structure Calibration Female Neural Networks Computer Artificial intelligence 0305 other medical science business Classifier (UML) Algorithms 030217 neurology & neurosurgery |
| Zdroj: | The Journal of International Medical Research |
| ISSN: | 1473-2300 0300-0605 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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