From v1.1, an implicit formulation of activation dynamics can be used to solve the muscle redundancy problem. Additionally, by using the activation dynamics model proposed by Raasch et al. (1997), we could introduce a nonlinear change of variables to exactly impose activation dynamics in a continuously differentiable form, omitting the need for a smooth approximation such as described in De Groote et al. (2016). A result of this change of variables is that muscle excitations are not directly accessible during the optimization. Therefore, we replaced muscle excitations by muscle activations in the objective function. This implicit formulation is described in \textit{De Groote F, Pipeleers G, Jonkers I, Demeulenaere B, Patten C, Swevers J, De Schutter J. A physiology based inverse dynamic analysis of human gait: potential and perspectives F. Computer Methods in Biomechanics and Biomedical Engineering (2009). Results from both formulations are very similar (differences can be attributed to the slightly different activation dynamics models and cost functions). However, the formulation with implicit activation dynamics (De Groote et al., (2009)) is computationally faster. This can mainly be explained by the omission of a tanh function in the constraint definition, whose evaluation is computationally expensive when solving the NLP.