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

Frontiers in Computational Neuroscience

David G. Lloyd¹  Leonardo eGizzi²  Massimo eSartori²  Dario eFarina² 

[1] Griffith University;Medical University Göttingen;

关键词: Lower Extremity;    Muscle Synergy;    EMG-driven modeling;    musculoskeletal modeling;    multiple degrees of freedom;    muscle dynamics;   

DOI  :  10.3389/fncom.2013.00079

来源: DOAJ

【 摘 要 】

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 is 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 excitation primitives) can be used as input drive for large musculoskeletal models across different human locomotor tasks. For this purpose, we extracted, with non-negative matrix factorization, five non-negative factors from a large sample of muscle EMG signals in two healthy subjects during four motor tasks including 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 EMG recordings (i.e. NRMSE = 0.16±0.07, and R2 = 0.78±0.18 across all tasks and subjects). Results support the hypothesis that dynamically 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.

【 授权许可】

Unknown