2026-09-19T12:14:34Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1520192023-09-01T03:40:50Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Herr, Hugh M.
Qiao, Junqing
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
2023-08-30T16:00:46Z
2023-08-30T16:00:46Z
2023-02
2023-08-16T20:36:17.344Z
https://hdl.handle.net/1721.1/152019
EMG-based prosthetic joint controllers have been an active research field for more than fifty years. However, several challenges remain to be addressed[9]. Electrodes positioning, controllers calibration, and controllers’ linear approximation error are the most challenging problems among them.
This thesis introduces three methods to solve those problems respectively. They are 1. A non-negative blind source separation algorithm named non-negative orthogonal decomposition(NOD). This algorithm aims to replace non-negative matrix factorization(NMF) for muscle motion base extraction. NOD recovers the source signal by finding the borders of the input signal and translating the borders onto the coordinate axis. The translated signals are the recovered signals. 2. An unsupervised algorithm for generating joint trajectories from EMG signals in reciprocating movements. The EMG signal and trajectory can be used for EMG-based prosthesis joint controller calibration. And 3. an innovative EMG-to-joint-position controller. It uses neural networks to compensate for the nonlinearity of the well-known bilinear model[2].
The NOD algorithm successfully extracted motion bases from the EMG signals. Compared with NMF, the motion bases are more independent and stable. The minimum-jerk-based trajectory generator generated smooth and biomimetic trajectories on intact subjects. The trajectories are close to the ground truth collected from the goniometer. The third model also has considerable improvement in joint angle accuracy over the linear muscle model.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
EMG methods for prosthesis ankle-subtalar free-space control
Thesis
application/pdf
Master
Master of Science in Media Arts and Sciences
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="4bdcd03c-574e-4995-9f1b-e48df8b2bf1c">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Title>EMG methods for prosthesis ankle-subtalar free-space control</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2023-02</PublicationDate>
<Authors>
<Author>
<DisplayName>Qiao, Junqing</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://rightsstatements.org/page/InC-EDU/1.0/</License>
<Abstract>EMG-based prosthetic joint controllers have been an active research field for more than fifty years. However, several challenges remain to be addressed[9]. Electrodes positioning, controllers calibration, and controllers’ linear approximation error are the most challenging problems among them.
This thesis introduces three methods to solve those problems respectively. They are 1. A non-negative blind source separation algorithm named non-negative orthogonal decomposition(NOD). This algorithm aims to replace non-negative matrix factorization(NMF) for muscle motion base extraction. NOD recovers the source signal by finding the borders of the input signal and translating the borders onto the coordinate axis. The translated signals are the recovered signals. 2. An unsupervised algorithm for generating joint trajectories from EMG signals in reciprocating movements. The EMG signal and trajectory can be used for EMG-based prosthesis joint controller calibration. And 3. an innovative EMG-to-joint-position controller. It uses neural networks to compensate for the nonlinearity of the well-known bilinear model[2].
The NOD algorithm successfully extracted motion bases from the EMG signals. Compared with NMF, the motion bases are more independent and stable. The minimum-jerk-based trajectory generator generated smooth and biomimetic trajectories on intact subjects. The trajectories are close to the ground truth collected from the goniometer. The third model also has considerable improvement in joint angle accuracy over the linear muscle model.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>