Prediction of closed-chain human arm dynamics in a crank-rotation task

The Effect of Crank Length Changes from Cycling Rehabilitation on Muscle Behaviors

“…Both experimental and simulation methods can be used to study the factors that influence muscle behavior during cycling. While human experiments are susceptible to sample bias, simulation studies allow the examination of complex body systems by changing only one parameter in the absence of noise from other confounding variables [ 23 ]. In addition, models can facilitate the study of all muscles in the absence of fatigue [ 24 , 25 ].…”

Biomechanical simulation of virtual physiological humans

“…The proposed algorithms and controllers are designed so as to handle constrained topologies. This is of significant importance because biological models (e.g., anatomical joints (Seth et al, 2010)) and interaction with the environment (e.g., crank-rotation tasks (Davoudabadi Farahani et al, 2016)) are modeled using constraints. We present two methods that operate in task space, namely a dynamically-based method for solving the Inverse Kinematics (IK) problem and a method for performing muscle driven dynamics simulations that closely reproduce experimental measurements of kinematics and Ground Reaction Forces (GRF).…”

“…Motion constraints are an important part of musculoskeletal systems, since biological models, such as anatomical joints (Seth et al, 2010), closed kinematic chains and interaction with the environment (e.g., crank-rotation tasks (Davoudabadi Farahani et al, 2016)) are modeled through constraints. The fundamental equations of differential variational principles for a constrained mechanical system lead to the following Differential Algebraic Equations (DAE) of index-3 ( (Eich, 1993))…”

“…As a matter of fact, many applications require a change of representation to solve a particular problem. Relevant examples include: the study of the mechanical capabilities of the human limbs (Valero-Cuevas, 2009a), where fundamental questions on the interaction of the nervous system with the physical world are addressed; the role of muscle synergies, their relation to the execution of a task (Kutch and Valero-Cuevas, 2012) and the influence they have in shaping the end point stiffness (Inouye and Valero-Cuevas, 2016); predictive simulation of closed-chain systems during execution of a crank-rotation task (Davoudabadi Farahani et al, 2016). In this context, researchers are interested in relating the meaningful, observable quantities (e.g., position, orientation, force, end point stiffness, etc.)…”

“…The proposed algorithms and controllers are designed so as to handle constrained multibody systems. This is of significant importance since many biological models (e.g., anatomical joints (Seth et al, 2010)) or experimental setups (e.g., crank-rotation tasks (Davoudabadi Farahani et al, 2016)) are modeled through constraints. Consequently, the presented framework can address models that use absolute (Cartesian) coordinates (Dziewiecki et al, 2014) instead of generalized coordinates (subsection 4.3.4) or systems that contain closed kinematic chains (De Sapio and Park, 2010) (subsection 4.3.5).…”