The concepts of muscle activity generation driven by upper limb kinematics
Abstract Background The underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the end-effector instead), which are similarly...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-06-01
|
| Series: | BioMedical Engineering OnLine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12938-023-01116-9 |
| _version_ | 1797795620077961216 |
|---|---|
| author | Marie D. Schmidt Tobias Glasmachers Ioannis Iossifidis |
| author_facet | Marie D. Schmidt Tobias Glasmachers Ioannis Iossifidis |
| author_sort | Marie D. Schmidt |
| collection | DOAJ |
| description | Abstract Background The underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the end-effector instead), which are similarly represented in our brains. This model is motivated by the known motion planning process in the central nervous system. That process incorporates the current body state from sensory systems and previous experiences, which might be represented as pre-learned inverse dynamics that generate associated muscle activity. Methods We develop a novel approach utilizing recurrent neural networks that are able to predict muscle activity of the upper limbs associated with complex 3D human arm motions. Therefore, motion parameters such as joint angle, velocity, acceleration, hand position, and orientation, serve as input for the models. In addition, these models are trained on multiple subjects (n=5 including , 3 male in the age of 26±2 years) and thus can generalize across individuals. In particular, we distinguish between a general model that has been trained on several subjects, a subject-specific model, and a specific fine-tuned model using a transfer learning approach to adapt the model to a new subject. Estimators such as mean square error MSE, correlation coefficient r, and coefficient of determination R 2 are used to evaluate the goodness of fit. We additionally assess performance by developing a new score called the zero-line score. The present approach was compared with multiple other architectures. Results The presented approach predicts the muscle activity for previously through different subjects with remarkable high precision and generalizing nicely for new motions that have not been trained before. In an exhausting comparison, our recurrent network outperformed all other architectures. In addition, the high inter-subject variation of the recorded muscle activity was successfully handled using a transfer learning approach, resulting in a good fit for the muscle activity for a new subject. Conclusions The ability of this approach to efficiently predict muscle activity contributes to the fundamental understanding of motion control. Furthermore, this approach has great potential for use in rehabilitation contexts, both as a therapeutic approach and as an assistive device. The predicted muscle activity can be utilized to guide functional electrical stimulation, allowing specific muscles to be targeted and potentially improving overall rehabilitation outcomes. |
| first_indexed | 2024-03-13T03:20:45Z |
| format | Article |
| id | doaj.art-f8498e96f2384942a953b203c4cfbbf0 |
| institution | Directory Open Access Journal |
| issn | 1475-925X |
| language | English |
| last_indexed | 2024-03-13T03:20:45Z |
| publishDate | 2023-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BioMedical Engineering OnLine |
| spelling | doaj.art-f8498e96f2384942a953b203c4cfbbf02023-06-25T11:23:13ZengBMCBioMedical Engineering OnLine1475-925X2023-06-0122112910.1186/s12938-023-01116-9The concepts of muscle activity generation driven by upper limb kinematicsMarie D. Schmidt0Tobias Glasmachers1Ioannis Iossifidis2Faculty of Electrical Engineering and Information Technology, Ruhr-University BochumFaculty of Computer Science, Ruhr-University BochumInstitute of Computer Science, University of Applied Science Ruhr WestAbstract Background The underlying motivation of this work is to demonstrate that artificial muscle activity of known and unknown motion can be generated based on motion parameters, such as angular position, acceleration, and velocity of each joint (or the end-effector instead), which are similarly represented in our brains. This model is motivated by the known motion planning process in the central nervous system. That process incorporates the current body state from sensory systems and previous experiences, which might be represented as pre-learned inverse dynamics that generate associated muscle activity. Methods We develop a novel approach utilizing recurrent neural networks that are able to predict muscle activity of the upper limbs associated with complex 3D human arm motions. Therefore, motion parameters such as joint angle, velocity, acceleration, hand position, and orientation, serve as input for the models. In addition, these models are trained on multiple subjects (n=5 including , 3 male in the age of 26±2 years) and thus can generalize across individuals. In particular, we distinguish between a general model that has been trained on several subjects, a subject-specific model, and a specific fine-tuned model using a transfer learning approach to adapt the model to a new subject. Estimators such as mean square error MSE, correlation coefficient r, and coefficient of determination R 2 are used to evaluate the goodness of fit. We additionally assess performance by developing a new score called the zero-line score. The present approach was compared with multiple other architectures. Results The presented approach predicts the muscle activity for previously through different subjects with remarkable high precision and generalizing nicely for new motions that have not been trained before. In an exhausting comparison, our recurrent network outperformed all other architectures. In addition, the high inter-subject variation of the recorded muscle activity was successfully handled using a transfer learning approach, resulting in a good fit for the muscle activity for a new subject. Conclusions The ability of this approach to efficiently predict muscle activity contributes to the fundamental understanding of motion control. Furthermore, this approach has great potential for use in rehabilitation contexts, both as a therapeutic approach and as an assistive device. The predicted muscle activity can be utilized to guide functional electrical stimulation, allowing specific muscles to be targeted and potentially improving overall rehabilitation outcomes.https://doi.org/10.1186/s12938-023-01116-9Electromyography (EMG)Inertial measurement unit (IMU)Neural networksMuscle activityMotion parametersVoluntary movement |
| spellingShingle | Marie D. Schmidt Tobias Glasmachers Ioannis Iossifidis The concepts of muscle activity generation driven by upper limb kinematics BioMedical Engineering OnLine Electromyography (EMG) Inertial measurement unit (IMU) Neural networks Muscle activity Motion parameters Voluntary movement |
| title | The concepts of muscle activity generation driven by upper limb kinematics |
| title_full | The concepts of muscle activity generation driven by upper limb kinematics |
| title_fullStr | The concepts of muscle activity generation driven by upper limb kinematics |
| title_full_unstemmed | The concepts of muscle activity generation driven by upper limb kinematics |
| title_short | The concepts of muscle activity generation driven by upper limb kinematics |
| title_sort | concepts of muscle activity generation driven by upper limb kinematics |
| topic | Electromyography (EMG) Inertial measurement unit (IMU) Neural networks Muscle activity Motion parameters Voluntary movement |
| url | https://doi.org/10.1186/s12938-023-01116-9 |
| work_keys_str_mv | AT mariedschmidt theconceptsofmuscleactivitygenerationdrivenbyupperlimbkinematics AT tobiasglasmachers theconceptsofmuscleactivitygenerationdrivenbyupperlimbkinematics AT ioannisiossifidis theconceptsofmuscleactivitygenerationdrivenbyupperlimbkinematics AT mariedschmidt conceptsofmuscleactivitygenerationdrivenbyupperlimbkinematics AT tobiasglasmachers conceptsofmuscleactivitygenerationdrivenbyupperlimbkinematics AT ioannisiossifidis conceptsofmuscleactivitygenerationdrivenbyupperlimbkinematics |