2023
Marie D. Schmidt; Tobias Glasmachers; Ioannis Iossifidis
The Concepts of Muscle Activity Generation Driven by Upper Limb Kinematics Journal Article
In: BioMedical Engineering OnLine, vol. 22, no. 1, pp. 63, 2023, ISSN: 1475-925X.
Abstract | Links | BibTeX | Tags: Artificial generated signal, BCI, Electromyography (EMG), Generative model, Inertial measurement unit (IMU), Machine Learning, Motion parameters, Muscle activity, Neural networks, transfer learning, Voluntary movement
@article{schmidtConceptsMuscleActivity2023,
title = {The Concepts of Muscle Activity Generation Driven by Upper Limb Kinematics},
author = {Marie D. Schmidt and Tobias Glasmachers and Ioannis Iossifidis},
url = {https://doi.org/10.1186/s12938-023-01116-9},
doi = {10.1186/s12938-023-01116-9},
issn = {1475-925X},
year = {2023},
date = {2023-06-24},
urldate = {2023-06-24},
journal = {BioMedical Engineering OnLine},
volume = {22},
number = {1},
pages = {63},
abstract = {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.},
keywords = {Artificial generated signal, BCI, Electromyography (EMG), Generative model, Inertial measurement unit (IMU), Machine Learning, Motion parameters, Muscle activity, Neural networks, transfer learning, Voluntary movement},
pubstate = {published},
tppubtype = {article}
}
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.