Quantitative data on the mechanics of diarthrodial joints and the function of ligaments are needed to better understand injury mechanisms, improve surgical procedures, and develop improved rehabilitation protocols. Therefore, experimental and computational approaches have been developed to determine joint kinematics and the in-situ forces in ligaments and their replacement grafts using human cadaveric knee and shoulder joints. A robotic/universal force-moment sensor testing system is used in our research center for the evaluation of a wide variety of external loading conditions to study the function of ligaments and their replacements; it has the potential to reproduce in-vivo joint motions in a cadaver knee. Two types of computational models have also been developed: a rigid body spring model and a displacement controlled spring model. These computational models are designed to complement and enhance experimental studies so that more complex loading conditions can be examined and the stresses and strains in the soft tissues can be calculated. In the future, this combined approach will improve our understanding of these joints and soft tissues during in-vivo activities and serve as a tool to aid surgical planning and development of rehabilitation protocols.
Richard E. Debski, Shon P. Darcy, and Savio L-Y. Woo
Heiko Wagner, Kim Joris Boström, Marc H.E. de Lussanet, Myriam L. de Graaf, Christian Puta, and Luis Mochizuki
medial force (positive for lateral direction), the anterior force (positive for anterior direction), and the superior force (positive for superior direction) in the prostheses were measured simultaneously at 100 Hz ( Heinlein et al., 2007 ). Computational Musculoskeletal Model For the computational
Gabriella M. Milef, Logan E. Miller, Daniella M. DiGuglielmo, Tanner D. Payne, Tanner M. Filben, Jillian E. Urban, and Joel D. Stitzel
from head impact telemetry system outputs for computational modeling . In: Tavares JMRS , Fernandes PR , eds. New Developments on Computational Methods and Imaging in Biomechanics and Biomedical Engineering . Lecture notes in computational vision and biomechanics . Springer International
David A. Rosenbaum, Ruud G.J. Meulenbroek, and Jonathan Vaughan
This paper presents the background, premises, and results of a model of movement planning. The model's central claims are fourfold: (a) A task is defined by a set of prioritized requirements, or what we call a constraint hierarchy; (b) movement planning works first by specifying a goal posture and then by specifying a movement to that goal posture; (c) movements have characteristic forms; and (d) movements can be shaped through simultaneous performance of different movements, even by the same effector. We review the model and then speculate on its implications for clinical concerns, especially spasticity.
Jonathan Vaughan, David A. Rosenbaum, and Ruud G. J. Meulenbroek
In this article, we review a model of the movement-planning processes that people use for direct reaching, reaching around obstacles, and grasping, and we present observations of subjects' repeated movements of the hand to touch 2 target locations, circumventing an intervening obstacle. The model defines an obstacle as a posture that, if adopted, would intersect with any part of the environment (including the actor himself or herself). The model finds a trajectory that is likely to bring the end-effector to me target by means of a one- or two-stage planning process. Each stage exploits the principles of instance retrieval and instance generation. In the first stage, a goal posture is identified, and the trajectory of a direct transition to that posture is tested for collision. If that direct movement has no collision, the movement to the target is immediately executed in joint space. If. however, the direct movement is foreseen to result in a collision, a second planning stage is invoked. The second planning stage identifies a via posture, movement through which will probably avoid the collision. Movement to and from the via posture is then superimposed on the main movement to the target so that the combined movement reaches the target without colliding with intervening obstacles. We describe the details of instance retrieval and instance generation for each of these planning stages and compare the model's performance with the observed kinematics of direct movements as well as movements around an obstacle. Then we suggest how the model might contribute to the study of movements in people with motor disorders such as spastic hemiparesis.
Ruud C.J. Meulenbroek, David A. Rosenbaum, and Jonathan Vaughan
In this paper we describe how a theory of posture-based motion planning recently applied to human grasping may contribute to the understanding of grasping pathology. The theory is implemented as a computer model rendered as a stick-figure animation capable of generating realistic multi-joint grasping movements. As shown here, the model can also be used to simulate grasping movements whose kinematics resemble those of grasps performed by people with spastic hemiparesis. The simulations demonstrate effects of: (a) reduced ranges of motion of arm joints on the size of the reachable workspace, (b) awkward starting postures on me time course of the hand closing around an object, (c) increased costs of joint rotations on movement time, and (d) addition of noise to biphasic joint rotations on the low-velocity phase of wrist transport.
Jonathan R. Kusins, Ryan Willing, Graham J.W. King, and Louis M. Ferreira
A computational elbow joint model was developed with a main goal of providing complimentary data to experimental results. The computational model was developed and validated using an experimental elbow joint phantom consisting of a linked total joint replacement. An established in-vitro motion simulator was used to actively flex/extend the experimental elbow in multiple orientations. Muscle forces predicted by the computational model were similar to the experimental model in 4 out of the 5 orientations with errors less than 7.5 N. Valgus angle kinematics were in agreement with differences less than 2.3°. In addition, changes in radial head length, a clinically relevant condition following elbow reconstruction, were simulated in both models and compared. Both lengthening and shortening of the radial head prosthesis altered muscle forces by less than 3.5 N in both models, and valgus angles agreed within 1°. The computational model proved valuable in cross validation with the experimental model, elucidating important limitations in the in-vitro motion simulator’s controller. With continued development, the computational model can be a complimentary tool to experimental studies by providing additional noninvasive outcome measurements.
JAB Journal of Applied Biomechanics 1065-8483 1543-2688 1 04 2020 36 2 10.1123/jab.2020.36.issue-2 EDITORIAL 10.1123/jab.2020-0035 COMPUTATIONAL MODEL 10.1123/jab.2018-0369 ORIGINAL RESEARCH 10.1123/jab.2019-0235 10.1123/jab.2019-0322 10.1123/jab.2019-0253 10.1123/jab.2019-0115 10.1123/jab.2019
MODEL 10.1123/jab.2018-0078 ORIGINAL RESEARCH 10.1123/jab.2018-0350 10.1123/jab.2019-0043 10.1123/jab.2018-0384 10.1123/jab.2018-0340 10.1123/jab.2018-0442 10.1123/jab.2018-0227 TECHNICAL NOTE 10.1123/jab.2018-0481 COMPUTATIONAL MODEL 10.1123/jab.2018-0078
MODEL 10.1123/jab.2021-0162 EDITORIAL 10.1123/jab.2021-0394 ORIGINAL RESEARCH 10.1123/jab.2020-0304 10.1123/jab.2021-0238 10.1123/jab.2021-0111 10.1123/jab.2021-0143 10.1123/jab.2021-0255 10.1123/jab.2020-0228 COMPUTATIONAL MODEL 10.1123/jab.2021-0162