The concepts of muscle activity generation driven by upper limb kinematics

BioMedical Engineering OnLine

Research

Tobias Glasmachers¹  Marie D. Schmidt²  Ioannis Iossifidis³ 

[1] Faculty of Computer Science, Ruhr-University Bochum, Bochum, Germany;Faculty of Electrical Engineering and Information Technology, Ruhr-University Bochum, Bochum, Germany;Institute of Computer Science, University of Applied Science Ruhr West, Mülheim an der Ruhr, Germany;Institute of Computer Science, University of Applied Science Ruhr West, Mülheim an der Ruhr, Germany;

关键词: Electromyography (EMG);    Inertial measurement unit (IMU);    Neural networks;    Muscle activity;    Motion parameters;    Voluntary movement;    Artificial generated signal;    Generative model;    Transfer learning;   

DOI  :  10.1186/s12938-023-01116-9

 received in 2022-10-27, accepted in 2023-05-16,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe 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.MethodsWe 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 R2 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.ResultsThe 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.ConclusionsThe 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.

【 授权许可】

CC BY   
© The Author(s) 2023

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