Functional Electrostimulation System for a Prototype of a Human Hand Prosthesis Using Electromyography Signal Classification by Machine Learning Techniques
Functional electrical stimulation (FES) has been proven to be a reliable rehabilitation technique that increases muscle strength, reduces spasms, and enhances neuroplasticity in the long term. However, the available electrical stimulation systems on the market produce stimulation signals with no per...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/12/1/49 |
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| author | Laura Orona-Trujillo Isaac Chairez Mariel Alfaro-Ponce |
| author_facet | Laura Orona-Trujillo Isaac Chairez Mariel Alfaro-Ponce |
| author_sort | Laura Orona-Trujillo |
| collection | DOAJ |
| description | Functional electrical stimulation (FES) has been proven to be a reliable rehabilitation technique that increases muscle strength, reduces spasms, and enhances neuroplasticity in the long term. However, the available electrical stimulation systems on the market produce stimulation signals with no personalized voltage–current amplitudes, which could lead to muscle fatigue or incomplete enforced therapeutic motion. This work proposes an FES system aided by machine learning strategies that could adjust the stimulating signal based on electromyography (EMG) information. The regulation of the stimulated signal according to the patient’s therapeutic requirements is proposed. The EMG signals were classified using Long Short-Term Memory (LSTM) and a least-squares boosting ensemble model with an accuracy of 91.87% and 84.7%, respectively, when a set of 1200 signals from six different patients were used. The classification outcomes were used as input to a second regression machine learning algorithm that produced the adjusted electrostimulation signal required by the user according to their own electrophysiological conditions. The output of the second network served as input to a digitally processed electrostimulator that generated the necessary signal to be injected into the extremity to be treated. The results were evaluated in both simulated and robotized human hand scenarios. These evaluations demonstrated a two percent error when replicating the required movement enforced by the collected EMG information. |
| first_indexed | 2024-03-08T10:43:31Z |
| format | Article |
| id | doaj.art-95d5ff8bc4c04f24ae45f47bb0f78d8c |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-08T10:43:31Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-95d5ff8bc4c04f24ae45f47bb0f78d8c2024-01-26T17:24:16ZengMDPI AGMachines2075-17022024-01-011214910.3390/machines12010049Functional Electrostimulation System for a Prototype of a Human Hand Prosthesis Using Electromyography Signal Classification by Machine Learning TechniquesLaura Orona-Trujillo0Isaac Chairez1Mariel Alfaro-Ponce2School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, MexicoInstitute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Zapopan 45201, MexicoInstitute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Zapopan 45201, MexicoFunctional electrical stimulation (FES) has been proven to be a reliable rehabilitation technique that increases muscle strength, reduces spasms, and enhances neuroplasticity in the long term. However, the available electrical stimulation systems on the market produce stimulation signals with no personalized voltage–current amplitudes, which could lead to muscle fatigue or incomplete enforced therapeutic motion. This work proposes an FES system aided by machine learning strategies that could adjust the stimulating signal based on electromyography (EMG) information. The regulation of the stimulated signal according to the patient’s therapeutic requirements is proposed. The EMG signals were classified using Long Short-Term Memory (LSTM) and a least-squares boosting ensemble model with an accuracy of 91.87% and 84.7%, respectively, when a set of 1200 signals from six different patients were used. The classification outcomes were used as input to a second regression machine learning algorithm that produced the adjusted electrostimulation signal required by the user according to their own electrophysiological conditions. The output of the second network served as input to a digitally processed electrostimulator that generated the necessary signal to be injected into the extremity to be treated. The results were evaluated in both simulated and robotized human hand scenarios. These evaluations demonstrated a two percent error when replicating the required movement enforced by the collected EMG information.https://www.mdpi.com/2075-1702/12/1/49functional electrostimulationLSTM classifierhuman hand prosthesis |
| spellingShingle | Laura Orona-Trujillo Isaac Chairez Mariel Alfaro-Ponce Functional Electrostimulation System for a Prototype of a Human Hand Prosthesis Using Electromyography Signal Classification by Machine Learning Techniques Machines functional electrostimulation LSTM classifier human hand prosthesis |
| title | Functional Electrostimulation System for a Prototype of a Human Hand Prosthesis Using Electromyography Signal Classification by Machine Learning Techniques |
| title_full | Functional Electrostimulation System for a Prototype of a Human Hand Prosthesis Using Electromyography Signal Classification by Machine Learning Techniques |
| title_fullStr | Functional Electrostimulation System for a Prototype of a Human Hand Prosthesis Using Electromyography Signal Classification by Machine Learning Techniques |
| title_full_unstemmed | Functional Electrostimulation System for a Prototype of a Human Hand Prosthesis Using Electromyography Signal Classification by Machine Learning Techniques |
| title_short | Functional Electrostimulation System for a Prototype of a Human Hand Prosthesis Using Electromyography Signal Classification by Machine Learning Techniques |
| title_sort | functional electrostimulation system for a prototype of a human hand prosthesis using electromyography signal classification by machine learning techniques |
| topic | functional electrostimulation LSTM classifier human hand prosthesis |
| url | https://www.mdpi.com/2075-1702/12/1/49 |
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