A musculoskeletal model of human locomotion driven by a low dimensional set of impulsive excitation primitives

Abstract

Human locomotion has been described as being generated by an impulsive (burst-like) excitation of groups of musculotendon units, with timing dependent on the biomechanical goal of the task. Despite this view being supported by many experimental observations on specific locomotion tasks, it is still unknown if the same impulsive controller (i.e., a low-dimensional set of time-delayed excitastion primitives) can be used as input drive for large musculoskeletal models across different human locomotion tasks. For this purpose, we extracted, with non-negative matrix factorization, five non-negative factors from a large sample of muscle electromyograms in two healthy subjects during four motor tasks. These included walking, running, sidestepping, and crossover cutting maneuvers. The extracted non-negative factors were then averaged and parameterized to obtain task-generic Gaussian-shaped impulsive excitation curves or primitives. These were used to drive a subject-specific musculoskeletal model of the human lower extremity. Results showed that the same set of five impulsive excitation primitives could be used to predict the dynamics of 34 musculotendon units and the resulting hip, knee and ankle joint moments (i.e., NRMSE = 0.18 ± 0.08, and R2 = 0.73 ± 0.22 across all tasks and subjects) without substantial loss of accuracy with respect to using experimental electromyograms (i.e., NRMSE = 0.16 ± 0.07, and R2 = 0.78 ± 0.18 across all tasks and subjects). Results support the hypothesis that biomechanically different motor tasks might share similar neuromuscular control strategies. This might have implications in neurorehabilitation technologies such as human-machine interfaces for the torque-driven, proportional control of powered prostheses and orthoses. In this, device control commands (i.e., predicted joint torque) could be derived without direct experimental data but relying on simple parameterized Gaussian-shaped curves, thus decreasing the input drive complexity and the number of needed sensors.

Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking

“…Several recent studies have explored how muscle synergies can be used to reduce control complexity in muscle force optimizations [8,10,[21][22][23]. These studies evaluated their models by predicting joint moments and muscle forces, whereas our study focused on predicting knee contact forces and muscle forces.…”

“…For walking, only three to seven neural commands are typically required to account for over 90% of the variability in as many as 32 processed lower extremity EMG signals [18][19][20]. To date, muscle synergy analysis has been used primarily for analyzing experimental EMG data, with only a few studies using it to inform muscle force optimizations [8,10,[21][22][23]. Use of experimentally determined neural commands to drive simulated muscle excitations may reduce control redundancy by limiting the excitations that can be constructed.…”

“…To address the muscle redundancy problem, numerous studies have used optimization methods to estimate a unique set of muscle (and sometimes joint contact) forces during walking [3][4][5][6][7][8][9][10][11]. The challenge for optimization methods is that the cost function being minimized by the human body (if it exists) remains unknown.…”

Robust Real-Time Musculoskeletal Modeling Driven by Electromyograms

“…This will underlie the employment of series elastic tendon elements as previously described [18] and ligaments, thus enabling the accurate estimation of a greater range of mechanical variables. Future work will also focus on the use of co-excitation primitives [16] for relaxing sensoryconstraints, i.e. the need for recording from large sets of muscles.…”

“…These are referred to as EMG-driven musculoskeletal models [12]- [16]. The authors and colleagues have developed and used them to show the ability of estimating internal body forces [12] as well as forces that depend on multi-muscle coordination, such as joint loadings [4], [17] or joint stiffness [18], [19], where inverse dynamics methods would be challenged [20], [21].…”