Continuous Grasping Force Estimation with Surface EMG Based on Huxley-type Musculoskeletal Model

• Published in: IEEE transactions on neural systems and rehabilitation engineering 2023-01, Vol.31, p.1-1
• Main Authors: Xu, Xiaolei, Deng, Hua, Zhang, Yi, Chen, Jingwei
• Format: Article
• Language: English
• Subjects: Computational modeling; Computer applications; Differential equations; Electromyography; Electromyography - methods; EMG filter; Estimation; Force; Force estimation; Grasping; Grasping force; Hand Strength; Humans; Huxley model; Kalman filters; Mathematical models; Model accuracy; Muscle, Skeletal - physiology; Muscles; Nonlinear differential equations; Parameter identification; parameter identify; Partial differential equations; Prostheses; Signal processing; spectral method; Spectral methods; surface EMG; Time lag
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2022.3214866

Continuous grasping force estimation based on electromyography (EMG) signals, is very useful in practical applications including prosthetic control and human force observation. However, implementing the practical grasping force estimation usually considers a trade-off between the computational precision and resources. Specifically, the estimation based on the Huxley-type muscle model reaches detailed approximation of physiological process at a cost of larger computational resources for solving nonlinear partial differential equations while the counterpart with a traditional Hill-type muscle model. In this article, we achieve the grasping force estimation based on a reduced Huxley-type musculoskeletal model with high accuracy yet low time delay. Leveraging on a balanced truncation method, we further reduce the dimensionality of the spectral method solution in the Huxley-type musculoskeletal model for the model simplification. In addition, we introduce the Kalman filter method to process the EMG signals obtained by an armband, yielding better real-time performances and accuracy compared to the signal treatment using the traditional EMG filter method. Moreover, we also implement an effective identification of the model parameters using a particle swarm method. Finally, we trained the model on the first day and made grasping force estimation experiments involved with three participants over the course of a month. We envision that this effective and practical method would further improve the practical applications in the field of grasping force estimation.