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.

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.

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.

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.