Estimation of muscle activation during different walking speeds with two mathematical approaches compared to surface EMG.

BACKGROUND

Muscle force estimation could improve clinical gait analysis by enhancing insight into causes of impairments and informing targeted treatments. However, it is not currently standard practice to use muscle force models to augment clinical gait analysis, partly, because robust validations of estimated muscle activations, underpinning force modelling processes, against recorded electromyography (EMG) are lacking.

RESEARCH QUESTION

Therefore, in order to facilitate future clinical use, this study sought to validate estimated lower limb muscle activation using two mathematical models (static optimisation SO, computed muscle control CMC) against recorded muscle activations of ten healthy participants.

METHODS

Participants walked at five speeds. Visual agreement in activation onset and offset as well as linear correlation (r) and mean absolute error (MAE) between models and EMG were evaluated.

RESULTS

MAE between measured and recorded activations were variable across speeds (SO vs EMG 15-68%, CMC vs EMG 13-69%). Slower speeds resulted in smaller deviations (mean MAE < 30%) than faster speeds. Correlation was high (r > 0.5) for only 11/40 (CMC) and 6/40 (SO) conditions (muscles X speeds) compared to EMG.

SIGNIFICANCE

Modelling approaches do not yet show sufficient consistency of agreement between estimated and recorded muscle activation to support recommending immediate clinical adoption of muscle force modelling. This may be because assumptions underlying muscle activation estimations (e.g. muscles' anatomy and maximum voluntary contraction) are not yet sufficiently individualizable. Future research needs to find timely and cost efficient ways to scale musculoskeletal models for better individualisation to facilitate future clinical implementation.