Voluntary EMG-to-force estimation with a multi-scale physiological muscle model

Abstract

BackgroundEMG-to-force estimation based on muscle models, for voluntary contraction has many applications in human motion analysis. The so-called Hill model is recognized as a standard model for this practical use. However, it is a phenomenological model whereby muscle activation, force-length and force-velocity properties are considered independently. Perreault reported Hill modeling errors were large for different firing frequencies, level of activation and speed of contraction. It may be due to the lack of coupling between activation and force-velocity properties. In this paper, we discuss EMG-force estimation with a multi-scale physiology based model, which has a link to underlying crossbridge dynamics. Differently from the Hill model, the proposed method provides dual dynamics of recruitment and calcium activation.MethodsThe ankle torque was measured for the plantar flexion along with EMG measurements of the medial gastrocnemius (GAS) and soleus (SOL). In addition to Hill representation of the passive elements, three models of the contractile parts have been compared. Using common EMG signals during isometric contraction in four able-bodied subjects, torque was estimated by the linear Hill model, the nonlinear Hill model and the multi-scale physiological model that refers to Huxley theory. The comparison was made in normalized scale versus the case in maximum voluntary contraction.ResultsThe estimation results obtained with the multi-scale model showed the best performances both in fast-short and slow-long term contraction in randomized tests for all the four subjects. The RMS errors were improved with the nonlinear Hill model compared to linear Hill, however it showed limitations to account for the different speed of contractions. Average error was 16.9% with the linear Hill model, 9.3% with the modified Hill model. In contrast, the error in the multi-scale model was 6.1% while maintaining a uniform estimation performance in both fast and slow contractions schemes.ConclusionsWe introduced a novel approach that allows EMG-force estimation based on a multi-scale physiology model integrating Hill approach for the passive elements and microscopic cross-bridge representations for the contractile element. The experimental evaluation highlights estimation improvements especially a larger range of contraction conditions with integration of the neural activation frequency property and force-velocity relationship through cross-bridge dynamics consideration.

A Pilot Study of Individual Muscle Force Prediction during Elbow Flexion and Extension in the Neurorehabilitation Field

“…Considering the muscle twitch response and linearity or non-linearity EMG–force relationship, we used a second-order discrete linear model and an exponential function to obtain muscle activations. Muscle activation dynamics is proven to be a mature method in current movement neuroscience and biomechanics research [13,17,18,27,33]. In our approach, we used a neural mapping method [13] to generate muscle activations for all seven muscles.…”

Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling

“…2). The EMG-driven musculoskeletal model component is developed based on previous work from the authors [13][14][15][26][27][28][29][30] as well as from other groups [31][32][33][34][35][36][37].…”

Real-Time Muscle Deformation via Decoupled Modeling of Solid and Muscle Fiber Mechanics

“…However, 1D passive properties can be easily embedded in our contractile element model to reinforce fibers and simulate eccentric contractions. f v (ε c ) is the force-velocity relationship that depends onε c (strain velocity), V max the maximum velocity of individual muscle fiber (set to 5(1 + a(t).f l (ε c )))/N bf ) and V sh a constant adimensional material parameter (set to 0.3) [9].…”

“…The muscles were activated in concentric contraction manner while both ends of the tendon are fixed. The activation is generated from EMG measurements using the typical procedure in biomechanics research [9], and is sent at each time step to the Hill contractile element model to generate force. Lc0 optimal fiber length was automatically set as initial fiber length, thus, each fiber in all the studies at t 0 is considered as in its optimal fiber length.…”