Pattern Recognition of EMG Signals by Machine Learning for the Control of a Manipulator Robot
Human Machine Interfaces (HMI) principles are for the development of interfaces for assistance or support systems in physiotherapy or rehabilitation processes. One of the main problems is the degree of customization when applying some rehabilitation therapy or when adapting an assistance system to t...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/9/3424 |
| _version_ | 1827671370556768256 |
|---|---|
| author | Francisco Pérez-Reynoso Neín Farrera-Vazquez César Capetillo Nestor Méndez-Lozano Carlos González-Gutiérrez Emmanuel López-Neri |
| author_facet | Francisco Pérez-Reynoso Neín Farrera-Vazquez César Capetillo Nestor Méndez-Lozano Carlos González-Gutiérrez Emmanuel López-Neri |
| author_sort | Francisco Pérez-Reynoso |
| collection | DOAJ |
| description | Human Machine Interfaces (HMI) principles are for the development of interfaces for assistance or support systems in physiotherapy or rehabilitation processes. One of the main problems is the degree of customization when applying some rehabilitation therapy or when adapting an assistance system to the individual characteristics of the users. To solve this inconvenience, it is proposed to implement a database of surface Electromyography (sEMG) of a channel in healthy individuals for pattern recognition through Neural Networks of contraction in the muscular region of the biceps brachii. Each movement is labeled using the One-Hot Encoding technique, which activates a state machine to control the position of an anthropomorphic manipulator robot and validate the response time of the designed HMI. Preliminary results show that the learning curve decreases when customizing the interface. The developed system uses muscle contraction to direct the position of the end effector of a virtual robot. The classification of Electromyography (EMG) signals is obtained to generate trajectories in real time by designing a test platform in LabVIEW. |
| first_indexed | 2024-03-10T03:41:00Z |
| format | Article |
| id | doaj.art-6c696b98c80d45c68b2e5af6e81e2b80 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:41:00Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6c696b98c80d45c68b2e5af6e81e2b802023-11-23T09:18:18ZengMDPI AGSensors1424-82202022-04-01229342410.3390/s22093424Pattern Recognition of EMG Signals by Machine Learning for the Control of a Manipulator RobotFrancisco Pérez-Reynoso0Neín Farrera-Vazquez1César Capetillo2Nestor Méndez-Lozano3Carlos González-Gutiérrez4Emmanuel López-Neri5Centro de Investigación, Innovación y Desarrollo Tecnológico UVM (CIIDETEC-UVM), Universidad del Valle de Mexico, Querétaro 76230, MexicoCentro de Investigación, Innovación y Desarrollo Tecnológico UVM (CIIDETEC-UVM), Universidad del Valle de Mexico, Querétaro 76230, MexicoCentro de Investigación, Innovación y Desarrollo Tecnológico UVM (CIIDETEC-UVM), Universidad del Valle de Mexico, Querétaro 76230, MexicoCentro de Investigación, Innovación y Desarrollo Tecnológico UVM (CIIDETEC-UVM), Universidad del Valle de Mexico, Querétaro 76230, MexicoCentro de Investigación, Innovación y Desarrollo Tecnológico UVM (CIIDETEC-UVM), Universidad del Valle de Mexico, Querétaro 76230, MexicoCentro de Investigación, Innovación y Desarrollo Tecnológico UVM (CIIDETEC-UVM), Universidad del Valle de Mexico, Querétaro 76230, MexicoHuman Machine Interfaces (HMI) principles are for the development of interfaces for assistance or support systems in physiotherapy or rehabilitation processes. One of the main problems is the degree of customization when applying some rehabilitation therapy or when adapting an assistance system to the individual characteristics of the users. To solve this inconvenience, it is proposed to implement a database of surface Electromyography (sEMG) of a channel in healthy individuals for pattern recognition through Neural Networks of contraction in the muscular region of the biceps brachii. Each movement is labeled using the One-Hot Encoding technique, which activates a state machine to control the position of an anthropomorphic manipulator robot and validate the response time of the designed HMI. Preliminary results show that the learning curve decreases when customizing the interface. The developed system uses muscle contraction to direct the position of the end effector of a virtual robot. The classification of Electromyography (EMG) signals is obtained to generate trajectories in real time by designing a test platform in LabVIEW.https://www.mdpi.com/1424-8220/22/9/3424EMGpattern recognitionmachine learningrobotcyber-physical systems |
| spellingShingle | Francisco Pérez-Reynoso Neín Farrera-Vazquez César Capetillo Nestor Méndez-Lozano Carlos González-Gutiérrez Emmanuel López-Neri Pattern Recognition of EMG Signals by Machine Learning for the Control of a Manipulator Robot Sensors EMG pattern recognition machine learning robot cyber-physical systems |
| title | Pattern Recognition of EMG Signals by Machine Learning for the Control of a Manipulator Robot |
| title_full | Pattern Recognition of EMG Signals by Machine Learning for the Control of a Manipulator Robot |
| title_fullStr | Pattern Recognition of EMG Signals by Machine Learning for the Control of a Manipulator Robot |
| title_full_unstemmed | Pattern Recognition of EMG Signals by Machine Learning for the Control of a Manipulator Robot |
| title_short | Pattern Recognition of EMG Signals by Machine Learning for the Control of a Manipulator Robot |
| title_sort | pattern recognition of emg signals by machine learning for the control of a manipulator robot |
| topic | EMG pattern recognition machine learning robot cyber-physical systems |
| url | https://www.mdpi.com/1424-8220/22/9/3424 |
| work_keys_str_mv | AT franciscoperezreynoso patternrecognitionofemgsignalsbymachinelearningforthecontrolofamanipulatorrobot AT neinfarreravazquez patternrecognitionofemgsignalsbymachinelearningforthecontrolofamanipulatorrobot AT cesarcapetillo patternrecognitionofemgsignalsbymachinelearningforthecontrolofamanipulatorrobot AT nestormendezlozano patternrecognitionofemgsignalsbymachinelearningforthecontrolofamanipulatorrobot AT carlosgonzalezgutierrez patternrecognitionofemgsignalsbymachinelearningforthecontrolofamanipulatorrobot AT emmanuellopezneri patternrecognitionofemgsignalsbymachinelearningforthecontrolofamanipulatorrobot |