An Adaptive Multi-Modal Control Strategy to Attenuate the Limb Position Effect in Myoelectric Pattern Recognition
Over the last few decades, pattern recognition algorithms have shown promising results in the field of upper limb prostheses myoelectric control and are now gradually being incorporated in commercial devices. A widely used approach is based on a classifier which assigns a specific input value to a s...
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| Published in | Sensors (Basel, Switzerland) Vol. 21; no. 21; p. 7404 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
07.11.2021
MDPI |
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| Online Access | Get full text |
| ISSN | 1424-8220 1424-8220 |
| DOI | 10.3390/s21217404 |
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| Abstract | Over the last few decades, pattern recognition algorithms have shown promising results in the field of upper limb prostheses myoelectric control and are now gradually being incorporated in commercial devices. A widely used approach is based on a classifier which assigns a specific input value to a selected hand motion. While this method guarantees good performance and robustness within each class, it still shows limitations in adapting to different conditions encountered in real-world applications, such as changes in limb position or external loads. This paper proposes an adaptive method based on a pattern recognition classifier that takes advantage of an augmented dataset—i.e., representing variations in limb position or external loads—to selectively adapt to underrepresented variations. The proposed method was evaluated using a series of target achievement control tests with ten able-bodied volunteers. Results indicated a higher median completion rate >3.33% for the adapted algorithm compared to a classical pattern recognition classifier used as a baseline model. Subject-specific performance showed the potential for improved control after adaptation and a ≤13% completion rate; and in many instances, the adapted points were able to provide new information within classes. These preliminary results show the potential of the proposed method and encourage further development. |
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| AbstractList | Over the last few decades, pattern recognition algorithms have shown promising results in the field of upper limb prostheses myoelectric control and are now gradually being incorporated in commercial devices. A widely used approach is based on a classifier which assigns a specific input value to a selected hand motion. While this method guarantees good performance and robustness within each class, it still shows limitations in adapting to different conditions encountered in real-world applications, such as changes in limb position or external loads. This paper proposes an adaptive method based on a pattern recognition classifier that takes advantage of an augmented dataset—i.e., representing variations in limb position or external loads—to selectively adapt to underrepresented variations. The proposed method was evaluated using a series of target achievement control tests with ten able-bodied volunteers. Results indicated a higher median completion rate >3.33% for the adapted algorithm compared to a classical pattern recognition classifier used as a baseline model. Subject-specific performance showed the potential for improved control after adaptation and a ≤13% completion rate; and in many instances, the adapted points were able to provide new information within classes. These preliminary results show the potential of the proposed method and encourage further development. Over the last few decades, pattern recognition algorithms have shown promising results in the field of upper limb prostheses myoelectric control and are now gradually being incorporated in commercial devices. A widely used approach is based on a classifier which assigns a specific input value to a selected hand motion. While this method guarantees good performance and robustness within each class, it still shows limitations in adapting to different conditions encountered in real-world applications, such as changes in limb position or external loads. This paper proposes an adaptive method based on a pattern recognition classifier that takes advantage of an augmented dataset-i.e., representing variations in limb position or external loads-to selectively adapt to underrepresented variations. The proposed method was evaluated using a series of target achievement control tests with ten able-bodied volunteers. Results indicated a higher median completion rate >3.33% for the adapted algorithm compared to a classical pattern recognition classifier used as a baseline model. Subject-specific performance showed the potential for improved control after adaptation and a ≤13% completion rate; and in many instances, the adapted points were able to provide new information within classes. These preliminary results show the potential of the proposed method and encourage further development.Over the last few decades, pattern recognition algorithms have shown promising results in the field of upper limb prostheses myoelectric control and are now gradually being incorporated in commercial devices. A widely used approach is based on a classifier which assigns a specific input value to a selected hand motion. While this method guarantees good performance and robustness within each class, it still shows limitations in adapting to different conditions encountered in real-world applications, such as changes in limb position or external loads. This paper proposes an adaptive method based on a pattern recognition classifier that takes advantage of an augmented dataset-i.e., representing variations in limb position or external loads-to selectively adapt to underrepresented variations. The proposed method was evaluated using a series of target achievement control tests with ten able-bodied volunteers. Results indicated a higher median completion rate >3.33% for the adapted algorithm compared to a classical pattern recognition classifier used as a baseline model. Subject-specific performance showed the potential for improved control after adaptation and a ≤13% completion rate; and in many instances, the adapted points were able to provide new information within classes. These preliminary results show the potential of the proposed method and encourage further development. |
| Author | Spieker, Veronika Ganguly, Amartya Haddadin, Sami Piazza, Cristina |
| AuthorAffiliation | 1 Munich Institute of Robotics and Machine Intelligence, Technical University of Munich, 80797 Munich, Germany; v.spieker@tum.de (V.S.); haddadin@tum.de (S.H.); cristina.piazza@tum.de (C.P.) 2 Department of Informatics, Technical University of Munich, 85748 Garching bei München, Germany |
| AuthorAffiliation_xml | – name: 1 Munich Institute of Robotics and Machine Intelligence, Technical University of Munich, 80797 Munich, Germany; v.spieker@tum.de (V.S.); haddadin@tum.de (S.H.); cristina.piazza@tum.de (C.P.) – name: 2 Department of Informatics, Technical University of Munich, 85748 Garching bei München, Germany |
| Author_xml | – sequence: 1 givenname: Veronika orcidid: 0000-0001-7720-7569 surname: Spieker fullname: Spieker, Veronika – sequence: 2 givenname: Amartya orcidid: 0000-0003-4093-1101 surname: Ganguly fullname: Ganguly, Amartya – sequence: 3 givenname: Sami orcidid: 0000-0001-7696-4955 surname: Haddadin fullname: Haddadin, Sami – sequence: 4 givenname: Cristina orcidid: 0000-0002-0358-8677 surname: Piazza fullname: Piazza, Cristina |
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| CitedBy_id | crossref_primary_10_1371_journal_pone_0291279 crossref_primary_10_1109_JSEN_2022_3169492 crossref_primary_10_3390_s24154840 |
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| SubjectTerms | Activities of daily living Adaptation Algorithms Calibration Classification Electromyography limb effect linear discriminant analysis Methods multi-modal control myoelectric control Pattern recognition Performance evaluation Prostheses Sensors upper-limb prostheses |
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| Title | An Adaptive Multi-Modal Control Strategy to Attenuate the Limb Position Effect in Myoelectric Pattern Recognition |
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