## 1 Introduction  
Lower limb musculoskeletal models (MSM) constitute a powerful simulation tool with numerous applications in the field of sports and rehabilitation. Despite the progress made in lower limb MSM, they still have some drawbacks which affect their validity [1] and their usefulness in clinical applications [2]. These drawbacks are related with three fundamental aspects: (i) have a precise and adjustable kinematic model for each patient; (ii) keep the model complexity as low as possible; and (iii) experimentally validate the model. First, joint kinematics must be able to accommodate specific rehabilitation movements and inter-subject variability. In effect, the knee kinematics model defines the estimated relative motion between relevant anatomical points, affecting the muscles' lever arms and the calculated forces, hence it is important to use models that can be adapted to different peoples and ranges of movement [3]. [PERSON] et al. conducted an exhaustive review of the different joint models used in MSM [4]. Simple knee models using the revolute pair do not adequately represent its kinematics [5]. In contrast, other complex models, such as those presented by [6], provide good movement estimates, but require anatomical information that can only be personalized using medical imaging techniques. However, the most widely used models represent the knee joint using a mobile axis of rotation, whose position and orientation are related to knee flexion angle. This relationship is obtained experimentally in in vitro studies, which limits its realism and customization capacity [7], mainly when applied to people with medical conditions [8; 9]. For this reason, other alternatives that are more adaptable to different subjects or conditions of movement have been developed. One approach is based on three-dimensional parallel mechanisms that attempt to simulate the geometry of the ligaments [10]. Unfortunately, they have the disadvantages of (i) needing complex personalization based on anatomical data that requires medical imaging [4], and (ii) being sensitive to the mechanical properties of the ligaments [11]. A simpler approach was proposed in [PERSON] et al. [12], which simulates the instantaneous axis of rotation through a four-bar mechanism obtained through a functional calibration procedure. Second, the models must offer an adequate representation of the contribution of muscle groups to joint movement, which is usually associated with considering a higher number of muscles. The majority of models used in practice apply schemes similar to those introduced by [PERSON] et al. [13], and improved by [PERSON] et al. [14] and [PERSON] et al. [15], with numerous groups of muscles. Generally, it is assumed that increasing the complexity of the model will improve its validity [16; 17]. However, increasing the number of muscles requires more information about their anatomical and physiological characteristics to calibrate the model [18]. In addition, the resolution of the problem of the redundancy of muscular actions becomes more difficult, requiring complex optimization algorithms with a high computational cost [19; 20]. For this reason, in many works, adjacent muscles are grouped, or only a single set of muscles is considered when solving the redundancy problem [21]. Finally, the validity of MSMs is perhaps the main limitation for their use in clinical applications. Currently, some practical rules for the verification and validation of MSMs have been established [22]. Other important clinimetric properties, such as the reliability of the estimates, have also been addressed [23]. However, most of the published works are limited to comparing estimates with other previous models or to indirect validations based on the coherence between the levels of EMG activity and the estimated forces [20; 24]. The works with direct validation, i.e., the magnitudes estimated by the model are compared directly with experimental measurements, are scarce, and are often limited to in vitro validations [25].