EMG-force modeling using parallel cascade identification.

Measuring force production in muscles is important for many applications such as gait analysis, medical rehabilitation, and human-machine interaction. Substantial research has focused on finding signal processing and modeling techniques which give accurate estimates of muscle force from the surface-recorded electromyogram (EMG). The proposed methods often do not capture both the nonlinearities and dynamic components of the EMG-force relation. In this study, parallel cascade identification (PCI) is used as a dynamic estimation tool to map surface EMG recordings from upper-arm muscles to the induced force at the wrist. PCI mapping involves generating a parallel connection of a series of linear dynamic and nonlinear static blocks. The PCI model parameters were initialized to obtain the best force prediction. A comparison between PCI and a previously published Hill-based orthogonalization scheme, that captures physiological behaviour of the muscles, has shown 44% improvement in force prediction by PCI (averaged over all subjects in relative-mean-square sense). The improved performance is attributed to the structural capability of PCI to capture nonlinear dynamic effects in the generated force.

Use of the fast orthogonal search method to estimate optimal joint angle for upper limb Hill-muscle models.

An important aspect of accurate representation of human movement is the ability to account for differences between individuals. The following paper proposes a methodology using Hill-based candidate functions in the fast orthogonal search (FOS) method to predict translational force at the wrist from flexion and extension torque at the elbow. Within this force-prediction framework, it is possible to implicitly estimate subject-specific physiological parameters of Hill-based models of upper arm muscles. Surface electromyography data from three muscles of the upper arm (biceps brachii, brachioradialis, and triceps brachii) were recorded from ten subjects, as they performed isometric contractions at varying elbow joint angles. Estimated muscle activation level and joint angle were utilized as inputs to the FOS model. Subject-specific estimates of optimal joint angles for the three muscles were determined via frequency analysis of the selected FOS candidate functions.

Contraction-based variations in upper limb EMG-force models under isometric conditions.

In this work, a previously developed model, which maps joint kinematic data and estimated muscle activation levels to net elbow joint torque, is trained with 4 groups of datasets in order to improve force estimation accuracy and gain insight into muscle behaviour. The training datasets are defined such that surface electromyogram (EMG) and force data are grouped within individual trials, across trials, within force levels and across force levels, and model performance is assessed. Average evaluation error ranged between 5% and 15%, with the lowest error observed for models trained with datasets grouped within separate force levels. Model error is further reduced when training datasets are grouped across data collection trials. Therefore, more accurate estimation of elbow joint behaviour can be accomplished by taking into account the functional requirements of muscle, and allowing for separate models to be developed accordingly.

Fast orthogonal search method to estimate upper arm Hill-based muscle model parameters.

We propose a methodology to estimate subject-specific physiological parameters of Hill-based models of upper arm muscles. The methodology uses Hill-type candidate functions in the Fast Orthogonal Search (FOS) method to predict force at the wrist during elbow flexion and extension. To this end, surface EMG data from three muscles of the upper arm were recorded from 5 subjects as they performed isometric contractions at different elbow joint angles. Estimated muscle activation level and joint angle were utilized as inputs to the FOS model to obtain subject-specific estimates of optimal joint angle the Gaussian shape parameter for the force-length relationship for each muscle.