## 5 Discussion
### Closeness of Fit and Potential Error Sources  
One goal of this research was to examine whether a method could be developed to yield reasonable estimates of in vivo muscle power from a unique study participant's EMG and anthropometry applied to a generic muscle model. The closeness of fit between the EFP and KIN power curves during the assessment interval indicates that the EFP power estimates are credible. In theory, a comparison of directly measured muscle power with the EMG-to-force processing model estimates would be the preferred way to validate this model. However, direct measurement of in vivo muscle power for muscles crossing the human knee is not possible. Thus, inverse dynamics are often used as a proxy. The fact that the net EFP and KIN knee power curves, when the muscles were known to be active as demonstrated by EMG, were closely correlated and comparable in magnitude supports the use of EFP to establish representative estimates of knee muscle power. Potentially, self-canceling errors could be present, yet the closeness of fit between the EFP and KIN power curves (when muscles were known to be active) indicates that the power estimates for individual muscles are acceptable. One important aspect of the RFP approach is that the power of individual muscles can be determined. Notably, all input variables to the model (electromechanical delay, percent maximum voluntary contraction) were determined a priori, and thus this approach was not a curve-fitting exercise. Figure 6: (**Upper**): Knee (solid blue line) and hip (solid red line) sagittal plane angles during gait. Percent gait cycle is shown on the abscissa. Positive values indicated extension of the respective joint(s). (**Lower**): Percent anatomic length (L\({}_{0}\)) for three hamstring muscles during gait. SMEMB (solid blue line), STEND (solid green line), and BFLH (solid red line) are shown. Previous efforts at EMG-to-force processing have generally been limited to predictions of muscle force (not power) [9; 10; 11; 12; 13; 38; 39; 40]. [PERSON] and colleagues made the largest contribution to our understanding of EMG-to-force processing [11; 12; 13; 14; 27]. They noted that \"what makes this line of work so interesting is that one can in fact'see' not the muscle itself and in that way gain insights about, not only how movements are performed, but also why they are performed in the way that they are\" [10]. A perfect neuromuscular model--one that can accurately predict muscle forces across the spectrum of human movements--does not exist. Like all models the EFP approach has built-in \"error sources\". It is possible that the predicted power (via EFP) is closer to actual power produced for some muscles and less so for others. Muscle fiber type, physiological cross-sectional area [52], optimal muscle fiber length [53; 78], and tendon slack length [51] are well-established and can influence muscle power. Other muscle properties that can influence power output include activation dynamics and MTU length rate-of-change and are less well-defined in gait and thus warrant further investigation. Estimates of muscle force per percent gait cycle are dependent on voluntary contraction efforts that represent 100% maximum force output. Observed muscle forces (and power) were dependent on accurate maximum muscle tests (MMT) to scale muscle-tendon unit forces. The obvious risk is that the MMT may not represent true maximum effort and could result in an overestimate of MTU forces and power. The muscles tested are large and powerful in this subject population, and it is technically difficult to obtain a \"true\" 100%MVC for several muscles tested. Substantial efforts were made to minimize this risk, including careful positioning of the limb during muscle testing [63] and using the larger value pre- or post-test MMT as a reference. Visual examination of the EFP and KIN power curves indicates that errors in muscle force and power amplitude were minor.