EMG ASSISTED OPTIMIZATION - A HYBRID APPROACH FOR ESTIMATING MUSCLE FORCES IN AN INDETERMINATE BIOMECHANICAL MODEL

被引:101
作者
CHOLEWICKI, J [1 ]
MCGILL, SM [1 ]
机构
[1] UNIV WATERLOO,FAC APPL HLTH SCI,DEPT KINESIOL,OCCUPAT BIOMECH LABS,WATERLOO N2L 3G1,ON,CANADA
关键词
Electromyography assisted optimization (EMGAO) - Physiologically observed muscle activation patterns;
D O I
10.1016/0021-9290(94)90282-8
中图分类号
Q6 [生物物理学];
学科分类号
071011 ;
摘要
There are two basic approaches to estimate individual muscle forces acting on a joint, given the indeterminacy of moment balance equations: optimization and electromyography (EMG) assisted. Each approach is characterized by unique advantages and liabilities. With this in mind, a new hybrid method which combines the advantages of both of these traditional approaches, termed 'EMG assisted optimization' (EMGAO), was described. In this method, minimal adjustments are applied to the individual muscle forces estimated from EMG, so that all moment equilibrium equations are satisfied in three dimensions. The result is the best possible match between physiologically observed muscle activation patterns and the predicted forces, while satisfying the moment constraints about all three joint axes. Several forms of the objective function are discussed and their effect on individual muscle adjustments is illustrated in a simple two-dimensional example.
引用
收藏
页码:1287 / 1289
页数:3
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