Prosthetic Hand Based on Human Hand Anatomy Controlled by Surface Electromyography and Artificial Neural Network
Humans have a complex way of expressing their intuitive intentions in real gestures. That is why many gesture detection and recognition techniques have been studied and developed. There are many methods of human hand signal reading, such as those using electroencephalography, electrocorticography, a...
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MDPI AG
2025-01-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/13/1/21 |
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| author | Larisa Dunai Isabel Seguí Verdú Dinu Turcanu Viorel Bostan |
| author_facet | Larisa Dunai Isabel Seguí Verdú Dinu Turcanu Viorel Bostan |
| author_sort | Larisa Dunai |
| collection | DOAJ |
| description | Humans have a complex way of expressing their intuitive intentions in real gestures. That is why many gesture detection and recognition techniques have been studied and developed. There are many methods of human hand signal reading, such as those using electroencephalography, electrocorticography, and electromyography, as well as methods for gesture recognition. In this paper, we present a method based on real-time surface electroencephalography hand-based gesture recognition using a multilayer neural network. For this purpose, the sEMG signals have been amplified, filtered and sampled; then, the data have been segmented, feature extracted and classified for each gesture. To validate the method, 100 signals for three gestures with 64 samples each signal have been recorded from 2 users with OYMotion sensors and 100 signals for three gestures from 4 users with the MyWare sensors. These signals were used for feature extraction and classification using an artificial neuronal network. The model converges after 10 sessions, achieving 98% accuracy. As a result, an algorithm was developed that aimed to recognize two specific gestures (handling a bottle and pointing with the index finger) in real time with 95% accuracy. |
| format | Article |
| id | doaj-art-2f88b1c2a92d4b80943f8749827f928c |
| institution | Kabale University |
| issn | 2227-7080 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-2f88b1c2a92d4b80943f8749827f928c2025-01-24T13:50:46ZengMDPI AGTechnologies2227-70802025-01-011312110.3390/technologies13010021Prosthetic Hand Based on Human Hand Anatomy Controlled by Surface Electromyography and Artificial Neural NetworkLarisa Dunai0Isabel Seguí Verdú1Dinu Turcanu2Viorel Bostan3Department Graphical Engineering, Universitat Politècnica de Valencia, 46022 Valencia, SpainDepartment Graphical Engineering, Universitat Politècnica de Valencia, 46022 Valencia, SpainDepartment of Software Engineering and Automatics, Technical University of Moldova, MD-2004 Chisina, MoldovaDepartment of Software Engineering and Automatics, Technical University of Moldova, MD-2004 Chisina, MoldovaHumans have a complex way of expressing their intuitive intentions in real gestures. That is why many gesture detection and recognition techniques have been studied and developed. There are many methods of human hand signal reading, such as those using electroencephalography, electrocorticography, and electromyography, as well as methods for gesture recognition. In this paper, we present a method based on real-time surface electroencephalography hand-based gesture recognition using a multilayer neural network. For this purpose, the sEMG signals have been amplified, filtered and sampled; then, the data have been segmented, feature extracted and classified for each gesture. To validate the method, 100 signals for three gestures with 64 samples each signal have been recorded from 2 users with OYMotion sensors and 100 signals for three gestures from 4 users with the MyWare sensors. These signals were used for feature extraction and classification using an artificial neuronal network. The model converges after 10 sessions, achieving 98% accuracy. As a result, an algorithm was developed that aimed to recognize two specific gestures (handling a bottle and pointing with the index finger) in real time with 95% accuracy.https://www.mdpi.com/2227-7080/13/1/21prosthetic handbionic handEMGgesture recognitionfeature extractionclassification |
| spellingShingle | Larisa Dunai Isabel Seguí Verdú Dinu Turcanu Viorel Bostan Prosthetic Hand Based on Human Hand Anatomy Controlled by Surface Electromyography and Artificial Neural Network Technologies prosthetic hand bionic hand EMG gesture recognition feature extraction classification |
| title | Prosthetic Hand Based on Human Hand Anatomy Controlled by Surface Electromyography and Artificial Neural Network |
| title_full | Prosthetic Hand Based on Human Hand Anatomy Controlled by Surface Electromyography and Artificial Neural Network |
| title_fullStr | Prosthetic Hand Based on Human Hand Anatomy Controlled by Surface Electromyography and Artificial Neural Network |
| title_full_unstemmed | Prosthetic Hand Based on Human Hand Anatomy Controlled by Surface Electromyography and Artificial Neural Network |
| title_short | Prosthetic Hand Based on Human Hand Anatomy Controlled by Surface Electromyography and Artificial Neural Network |
| title_sort | prosthetic hand based on human hand anatomy controlled by surface electromyography and artificial neural network |
| topic | prosthetic hand bionic hand EMG gesture recognition feature extraction classification |
| url | https://www.mdpi.com/2227-7080/13/1/21 |
| work_keys_str_mv | AT larisadunai prosthetichandbasedonhumanhandanatomycontrolledbysurfaceelectromyographyandartificialneuralnetwork AT isabelseguiverdu prosthetichandbasedonhumanhandanatomycontrolledbysurfaceelectromyographyandartificialneuralnetwork AT dinuturcanu prosthetichandbasedonhumanhandanatomycontrolledbysurfaceelectromyographyandartificialneuralnetwork AT viorelbostan prosthetichandbasedonhumanhandanatomycontrolledbysurfaceelectromyographyandartificialneuralnetwork |