In-Vitro Shoulder Simulators

Dowson and Jobbins (1988) defined a simulator as “any device or system that simulates specific conditions or the characteristics of a real process for the purpose of research or

operator training” (p.111). Applied to in-vitro biomechanical research, a simulator is a

system which replicates the physiologic conditions, such as loading and motion, experienced by a joint complex and the environment it functions in. Previous

investigators have employed in-vitro simulators using full cadaveric shoulder specimens

as a means to increase the efficacy and accuracy of their biomechanical research; however, their level of accuracy and the types of testing employed have varied widely.

These simulators can be classified as (1) static, or (2) ‘dynamic’1, where dynamic can be

sub-classified into passive or active motion simulators. The majority of research has focused on the assessment of static biomechanical outcomes, and passive, investigator driven functional assessments. A far smaller group of simulators have addressed the

replication of in-vivo active muscle driven motion. Each of these methods represents an

important opportunity for broadening our understanding of basic shoulder biomechanics and the biomechanics of clinically relevant questions related to orthopaedic injury, dysfunction, and reconstruction.

1 _{where this simply denotes motion and not the traditional definition of ‘dynamic’}

1.7.1

Static Shoulder Simulators

The purpose of a static joint simulator is to replicate the conditions of a joint in one discrete joint configuration. As a result, these systems cannot assess how outcomes change as the joint configuration changes, but are well suited to describing discrete biomechanical variables which exist at a given joint pose such as muscle lines of action or moment arms. These systems have been widely used in shoulder research (Ackland et al., 2008; Alexander et al., 2013; Karduna et al., 1996; Kelkar et al., 2001; L. J. Soslowsky et al., 1992) and typically employ loading of appropriate muscle groups or direct loading of the bones in order to articulate the joint.

1.7.2

Dynamic Shoulder Simulators

Dynamic simulators differ from static simulators in that they seek to replicate the motion of the joint in addition to its loading and environmental conditions. Thus, these simulators are capable of evaluating outcome variables continuously across a given motion. Previous simulators have replicated this motion both passively and actively as outlined below.

1.7.2.1 Passive Shoulder Motion Simulators

Passive simulation of the shoulder involves the assessment of the joint’s motion and function through a series of tests performed by the investigator. These tests are carried out using a minimally dissected shoulder joint complex on a simulator designed to replicate a

predefined set of environmental conditions drawn from the in-vivo state. Passive

assessments commonly involve the evaluation of the joint’s ranges of motion, articular contact mechanics, and stability. Although not as physiologically accurate as the active motion simulation discussed in the following section, passive simulation is a very important part of fully assessing the shoulder as it enables investigators to perform tests similar to those used clinically in the evaluation of this joint.

Passive in-vitro shoulder simulators expanded beyond simple bench top studies in the

and load cell hardware (Harryman et al., 1990; Itoi, Motzkin, Morrey, & An, 1994) which permitted the recording of loads and joint kinematics during passive motion. These early devices, however, relied entirely on the experimenter’s ability to consistently manipulate the humerus relative to the scapula and thus resulted in large variability in their results. The following years saw the emergence of systems that enabled greater control over the load applied to individual degrees of freedom during the monitoring of joint translations (Warner, Deng, Warren, & Torzilli, 1992); however, these did not accurately model physiologic bone configurations and did not dynamically load the muscles of the shoulder complex. Further developments by Itoi et al. (1994) resulted in a simulator capable of properly orienting the constituent bones of the shoulder relative to gravity while also loading relevant rotator cuff muscle groups in physiologic patterns. More recently, these types of systems have allowed an increasing number of muscle groups to be loaded and a greater range of outcome variables to be assessed (Ackland & Pandy, 2011; D. C. Ackland et al., 2008; Alexander et al., 2013; Yu, McGarry, Lee, Duong, & Lee, 2005). Subsequently, other groups began to acquire the ability to perform objective assessments of joint motion by recording loads while pneumatic and robotic systems moved the shoulder through predefined motion pathways (Debski, Wong, Woo, Sakane, Fu, & Warner, 1999a; McMahon et al., 2003). Despite the widespread development and use of these simulators, no one simulator has been developed which possesses all of the key

design features required to improve the accuracy of the replicated environment (i.e.

simulated muscle loading, scapular motion, etc…) and permit objective evaluations of

physiologically meaningful outcome variables (i.e. develop a mechanism to permit

isolation of individual DOF, implement a continuous tracking system, etc…).

1.7.2.2 Active Shoulder Motion Simulators

Although passive shoulder motion simulators can provide important information regarding joint function and stability, the external validity of these results are limited

because the motions are not driven by muscle loading as is the case in-vivo. Therefore, a

number of groups have focused on the development of active motion simulators which rely entirely on muscle loading to produce glenohumeral motion. Kedgley et al. (2007)

have additionally shown that the implementation of a simulator which uses continually variable muscle forces to drive shoulder motion can produce motions with higher repeatability than those performed passively. This higher repeatability in turn increases the statistical power of the findings and their physiologic validity, because the kinetics of the joint are more closely replicated.

Active motion can be produced through one of three control methods: (1) muscle load control, (2) position control, (3) computational model driven muscle loading. Muscle load control has been the most widely implemented technique, whereby the muscle primarily responsible for the motion of interest, termed the ‘prime mover,’ is used to define a set of muscle loading ratios. Despite its widespread use, muscle load control is of limited value as it has difficulty producing slow, smooth, and repeatable motion (Dunning et al., 2001). Position control has the potential to produce much more repeatable motion than load

control, but traditionally has produced loads that are less representative of the true in-vivo

loading environment (Dunning et al., 2001). At the time of writing, there are no reports in the literature of the third method being implemented in a manner which can produce smooth motion. This may relate to the computational complexity of implementing a system that can use a computational model while also adjusting appropriately given real time feedback.

Cain et al. and Soslowsky et al. were the first to develop active simulators; however, they used these to place the joint in discrete positions and record outcomes rather than produce continuous motions (Cain, Mutschler, Fu, & Lee, 1987; L. Soslowsky et al., 1992). Debski et al. (1995) and Wuelker et al. (1994) concurrently developed the first simulators capable of achieving dynamic joint motions by using an open loop muscle load controller in which the muscle length of the ‘prime mover’ was shortened at a constant rate, and the secondary muscle loads were apportioned based on this muscle’s measured load. These systems, however, did not adjust scapular orientation, attempt to maintain a constant rotational velocity, or, in the case of Desbki et al., use physiologic muscle loading ratios, all of which affected the accuracy and repeatability of the reported kinematic outcomes

muscle loading ratios but have continued to depend on a constant prime mover velocity to achieve repeatable motions (Halder, Zhao et al., 2001; Halder et al., 2001; Malicky, Soslowsky, Blasier, & Shyr, 1996; McMahon et al., 1995).

Muscle loading ratios were initially chosen as constant values across an entire motion,

thus neglecting variations observed in in-vivo electromyographic (EMG) studies;

however, Kedgley et al. (2007) demonstrated that the use of continuously varying loading ratios produces a more physiologically accurate loading model. Despite these developments, these simulators continue to depend on muscle loading ratios drawn from the literature and as a result, the ability of these systems to control all three rotational DOF of the shoulder is limited, since population average ratios are unlikely to produce exactly the desired motion in any given specimen. As well, the data used to construct muscle loading ratios is limited to a very small set of motions which have been

investigated in-vivo and thus these systems are somewhat limited to simulating these

motions. There remains to be a simulator described in the literature which uses motion control through real time kinematic feedback to achieve improved repeatability and control of all three of the shoulder’s rotations.