Research Papers

Development and Performance Evaluation of a Multi-PID Muscle Loading Driven In Vitro Active-Motion Shoulder Simulator and Application to Assessing Reverse Total Shoulder Arthroplasty

[+] Author and Article Information
Joshua William Giles

University of Western Ontario,
268 Grosvenor Street,
London, ON N6A 4V2, Canada
e-mail: giles.joshgiles@gmail.com

Louis Miguel Ferreira

University of Western Ontario,
268 Grosvenor Street,
London, ON N6A 4V2, Canada
e-mail: Louis.Ferreira@sjhc.london.on.ca

George Singh Athwal

Roth McFarlane Hand and Upper Limb Centre,
268 Grosvenor Street,
London, ON N6A 4V2, Canada
e-mail: George.Athwal@sjhc.london.on.ca

James Andrew Johnson

University of Western Ontario,
268 Grosvenor Street,
London, ON N6A 4V2, Canada
e-mail: jim.johnson@sjhc.london.on.ca

Manuscript received May 5, 2014; final manuscript received October 10, 2014; accepted manuscript posted October 16, 2014; published online November 3, 2014. Assoc. Editor: Brian D. Stemper.

J Biomech Eng 136(12), 121007 (Nov 03, 2014) (10 pages) Paper No: BIO-14-1194; doi: 10.1115/1.4028820 History: Received May 05, 2014; Revised October 10, 2014; Accepted October 16, 2014

In vitro active shoulder motion simulation can provide improved understanding of shoulder biomechanics; however, accurate simulators using advanced control theory have not been developed. Therefore, our objective was to develop and evaluate a simulator which uses real-time kinematic feedback and closed loop proportional integral differential (PID) control to produce motion. The simulator’s ability to investigate a clinically relevant variable—namely muscle loading changes resulting from reverse total shoulder arthroplasty (RTSA)—was evaluated and compared to previous findings to further demonstrate its efficacy. Motion control of cadaveric shoulders was achieved by applying continuously variable forces to seven muscle groups. Muscle forces controlling each of the three glenohumeral rotational degrees of freedom (DOF) were modulated using three independent PID controllers running in parallel, each using measured Euler angles as their process variable. Each PID controller was configured and tuned to control the loading of a set of muscles which, from previous in vivo investigations, were found to be primarily responsible for movement in the PID’s DOF. The simulator’s ability to follow setpoint profiles for abduction, axial rotation, and horizontal extension was assessed using root mean squared error (RMSE) and average standard deviation (ASD) for multiple levels of arm mass replacement. A specimen was then implanted with an RTSA, and the effect of joint lateralization (0, 5, 10 mm) on the total deltoid force required to produce motion was assessed. Maximum profiling error was <2.1 deg for abduction and 2.2 deg for horizontal extension with RMSE of <1 deg. The nonprofiled DOF were maintained to within 5.0 deg with RMSE <1.0 deg. Repeatability was high, with ASDs of <0.31 deg. RMSE and ASD were similar for all levels of arm mass replacement (0.73–1.04 and 0.14–0.22 deg). Lateralizing the joint’s center of rotation (CoR) increased total deltoid force by up to 8.5% body weight with the maximum early in abduction. This simulator, which is the first to use closed loop control, accurately controls the shoulder’s three rotational DOF with high repeatability, and produces results that are in agreement with previous investigations. This simulator’s improved performance, in comparison to others, increases the statistical power of its findings and thus its ability to provide new biomechanical insights.

Copyright © 2014 by ASME
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Fig. 1

Shoulder active motion simulator. Photograph of simulator with a right shoulder mounted. Note the low friction pneumatic actuators (a), low friction cable guides (b), mass replacement system (c), optical trackers on the humerus and scapula (d), adjustable scapula pot (e), and DC servomotor and linkage system which drives scapular rotations (f).

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Fig. 2

Control system flow diagram. Diagram illustrates the system's input and output variables and how data flows through it. Note that dashed black arrows denote kinematic inputs; gray arrows indicate ratio based data (i.e., input muscle loading ratios or muscle force distributions output from a PID) and gray boxes are operations (i.e., PID control loops) with ratio outputs; solid black arrows indicate force values and solid black boxes indicate operations which produce or modulate a force command.

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Fig. 3

Example muscle loads produced by motion controller. Shown are a set of muscle loads produced during abduction in the scapular plane for an intact shoulder. Note that in this specimen, 55 deg glenohumeral abduction combined with scapular rotation corresponded to the arm parallel to the ground.

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Fig. 4

Abduction and horizontal extension profiling accuracy and repeatability. (a) Accuracy of the simulator in following a predefined abduction profile in the scapular plane. The profile begins at the resting position (∼10 deg) and ends with the arm parallel to the ground. (b) Accuracy of the active motion simulator in following a predefined horizontal extension profile with the arm parallel to the ground and externally rotated. The profile begins with the humerus in the scapular plane and ends 35 deg posterior. The “difference” series on the secondary axis is the difference between the profile and resulting motion. The thin black standard deviation lines across the motion demonstrate the system’s high repeatability.

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Fig. 5

Effect of variations in specimen’s size-to-mass ratio. Shown is the simulator’s response to a ±40% change in the specimen’s mass which is used to replicate subjects with varying size-to-mass ratios (i.e., BMI). The dashed lines represent the respective setpoint profiles for this abduction motion while the solid lines are the resulting motions.

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Fig. 6

Effect of varying proportional and integral gains on controller characteristics. (a) Response of a proportional only controller and how response varies with increasing gain values during abduction in the scapular plane. Graphs (b) and (c) demonstrate the effects of increasing the integral time component of the “optimal” PID controller during abduction and horizontal extension, respectively. Similarly, (d) and (e) demonstrate the effects of decreasing integral time during abduction and horizontal extension, respectively.

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Fig. 7

Simulator’s response to scapular disturbance. This graph demonstrates the simulator’s ability to minimize the effect of disturbances, and quickly reject any disturbance in glenohumeral orientation. Note that the Disturbance data series is plotted on the secondary axis.

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Fig. 8

Total deltoid muscle force for varying levels of glenosphere lateralization. Shown are the total deltoid loads for varying reverse total shoulder glenosphere lateralization levels. These data demonstrate that the simulator’s controller is sensitive to changes in joint geometry.




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