An understanding of forces that act on the shoulder joint is important for designing, testing, and evaluating shoulder arthroplasty products. Last year, we presented data describing upper arm motion during eight in-situ hours of occupational and recreational tasks. Using that data the associated humeral head joint forces were calculated with an upper extremity model in OpenSim.

Five subjects from a nonrandom sampling of occupations wore the Inertial Measurment Units during a four hour period while at their work place performing their normal work duties and then during the four hour period of non-work activities immediately following. An unscented Kalman filter (UKF) was used to produce the 3D humeral – thoracic angles at 128 Hz from the IMU data.

Because of the very large number of data points collected with the IMUs, ninety samples of twenty second duration were randomly selected from each four hour collection period. Using the sampled files, the time scales of the sampled files were scaled by a factor of five and then analyzed with the SUEM static optimization and joint reaction features. Not every sample file could be modeled resulting in an average number of sampled files of 66.7 per subject and condition (work/recreation).

The humeral – thoracic angles were then used as input to the Stanford Upper Extremity Model (SUEM) in the OpenSim environment. The SUEM model allowed 2 rotation degree of freedom (rdof) for the forearm (flexion twist), 3 rdof at the humeral – scapular joint, and predicted scapular motion based on the humeral – thoracic elevation angle. All models were run for an assumed 80 kg body weight and included the bone mass of the scapula, clavical, humerus, radius, and ulna, but none of the soft tissue mass. Shoulder muscles were represented by 15 actuators: three heads for each of the deltoid, latissimus dorsi, and pectoralis, and 1 head each for the coracobrachialis, infraspinatous, subscapularis, supraspinatous, termes major, and teres minor.

The 5^th, 50^th, and 95^th percentile values of each force component acting on the humeral head from each sampled file for each subject and condition were calculated and the distribution of forces was plotted as a histogram. The overall mean and standard deviation for the 5^th, 50^th, and 95^th percentile values were also determined.

Of the A-P and S-I force components, anterior and inferior directed force components were larger than the posterior and superior directed force components. For the M-L force component, the forces were nearly exclusively directed in the medial direction. The 5^th and 95^th percentile forces during these subjects' ADL were generally lower than those described by Westerhoff 2009, suggesting that within the limitations of the modeling assumptions, loading experienced during in-situ ADL may be different than loading during laboratory simulation of representative motions.

Shortened humeral stem implants can be advantageous as they preserve more of the patient's bone and are not limited by the canal for placement in the proximal body. However, traditional longer stems may help stabilize the implant through interaction with the dense cortical bone of the canal. We developed an FEA model to gage the contributions of design features such as stem length, coatings, and interference fit.

Models were constructed in FEMAP and solved using the NX Nastran advanced nonlinear static solver. The Turon (DJO Surgical) implant geometry was imported from a Solidworks CAD file and bone geometry was taken from a statistical shape model by Materialise representing the mean humeral geometry of 95 healthy humeri (avg age = 69.9 years). Implant and cancellous bone were considered to be linear homogeneous materials, and the cortical shell was modeled as orthotropic. Interference fits between the implant and cancellous bone surfaces were modeled using the gap feature of NX Nastran with friction coefficients corresponding to the surface finish. Loading was applied through a control node located at the center for the replacement head. Two loading conditions were analyzed, one representing torsion about the neck axis with a magnitude of 3140 Nm and one representing the peak load vector during activities of daily living.

Using resection plane nodes at the intersection of the implant and bone, the histograms of micromotion and the associated 5^th, 50^th, and 95^th percentile values were calculated. For a traditional length stem, the dominate effect on the predicted micromotion at the resection plane was the interference fit in the coating region. The contribution of a traditional length stem to resection plane micromotion was complex and depended on the presence of the stem and the amount of interference fit in the coating region.

A design modification to the DJO Linear hip stem was performed to facilitate use of the stem with the minimally invasive direct anterior approach. While the main design consideration was to reduce the overall stem length, it was also important to increase congruency of the implant and proximal cortical bone to ensure initial stability.

An initial design attempt produced a geometry that was difficult to insert into the femur; therefore, reconstructed digital models of the femur (ADaMs by Materialise) were obtained and used to delineate the best fit implant cross section. The ADaMs models were constructed from 74 CT scans taken from northern Europeans undergoing investigations for cardio-vascular conditions. Using equivalency points, models representing the bone mean, ±1σ, and ±2σ were constructed. The ADaMs models are pictured in Figure 1.

After importing the ADaMs models in the Solidworks CAD environment, the existing Linear stem was ideally positioned in the femur model and equally spaced planes parallel to the resection plane were defined as shown in Figure 2. At each plane, the shape of the cortical bone was determined and then used to define an implant cross section that was congruent to the bone, at least as large as the Linear hip stem, and symmetric about its midline. After using the base ADaMs models to drive the design's geometry, the final design fit was validated for very small patients using a hypothetical size −4σ extrapolation of the ADaMs models.

The digital reconstructions improved the design process by providing accurate, tangible models of the actual femur geometry. From these models, the design team was able to visualize how implant geometry should be constructed to optimize congruency, symmetry, and favorable insertion characteristics. Additionally, the ADaMs models served to validate the design for a challenging condition and as a starting point for computer simulations that were able to predict the insertion difficulty encountered in the initial, pre ADaMs model design. The final redesign was launched in the US in 2014 as the TaperFill hip stem.

Measurements of shoulder kinematics during activities of daily living (ADL) can be used to evaluate patient function before and after treatment and help define device testing conditions. However, due to the difficulties of making 3D motion measurements outside of laboratory conditions, there are few reports of measured shoulder 3D kinematics during ADL. The purpose of this study was to demonstrate the feasibility of using wearable inertial measurement units (IMUs) to track shoulder joint angles.

A nonrandom sample of 5 subjects with normal shoulders was selected based on occupation. The occupations were: dental hygienist, primary school teacher, mechanical project engineer, administrative assistant, and retail associate. Subjects wore two OPAL IMUs (APDM, Portland OR) as shown in Figure 1 on the sternum and on the upper arm for approximately 4 hours while at their workplace performing their normal work place activities and then up to 4 hours while off-work.

Orientation angles from IMUs have traditionally been estimated by integrating gyroscope data and calculating inclination angles relative to gravity with accelerometers. A significant problem is that inaccuracies inherent in the measurements can degrade accuracy. In this study, we used an Unscented Kalman Filter (UKF) with IMU output to track shoulder angles. The UKF mitigates the effect of random drift by incorporating domain knowledge about the shoulder normal range of motion, and the gyroscope and accelerometer characteristics into the state-space models. Initially, in the horizontal plane, without gravity measurements from the accelerometer to aid the gyroscope data, there were unacceptable errors in transverse rotation. To mitigate this error, additional constraints were applied to model gyroscope drift and a zero velocity update strategy was included. These additions decreased tracker errors in heading by 63%. The resulting accuracy with the modified tracker in all motion planes was about 2° (Figure 2).

Subjects commented that the IMUs were well tolerated and did not interfere with their ability to perform tasks in a normal manner. The overall averaged 95th percentile angles (Figure 3) were: flexion 128.8°, adduction 128.4°, and external rotation 69.5°. These peaks angles are similar to other investigator's reports using laboratory simulations of ADL tasks measured with optical and electromagnetic technologies, though this study's observations did show 17% greater extension and 40% greater adduction. Additionally, in these observations, occurrences of maximal internal rotation were rare compared to maximal external rotation and when maximum external rotation did occur, it was in combination with an average flexion angle of 103°. Finally, by performing a Fourier transform of the arm angles and using the 50th percentile frequency the number of arm cycles in a 10 year period was calculated at over 600,000 cycles.

Application of the UKF with the additional drift correction made substantial improvements in shoulder tracking performance and this feasibility data suggests that IMUs with the UKF are suitable for extended use outside of laboratory settings. The motion data collected provides a novel description of arm motion during ADLs including estimating the cycle count of the upper arm at more than 600,000 cycles over 10 years.

The Stanford Upper Extremity Model (SUEM) (Holzbauer, Murray, Delp 2005, Ann Biomed Eng) includes the major muscles of the upper limb and has recently been described in scientific literature for various biomechanical purposes including modeling the muscle behavior after shoulder arthroplasty (Hoenecke, Flores-Hernandez, D'Lima 2014, J Shoulder Elbow Surg; Walker, Struk, Banks 2013, ISTA Proceedings). The initial publication of the SUEM compared the muscle moment arm predictions of the SUEM against various moment arm studies and all with the scapula fixed. A more recent study (Ackland, Pak, and Pandy 2008, J Anat) is now available that can be used to compare SUEM moment arm predictions to cadaver data for similar muscle sub-regions, during abduction and flexion motions, and with simulated scapular motion.

SUEM muscle moment arm component vectors were calculated using the OpenSim Analyze Tool for an idealized abduction and an idealized flexion motion from 10° to 90° that corresponded to the motions described in Ackland for the cadaver arms. The normalized, averaged muscle moment arm data for the cadavers was manually digitized from the published figures and then resampled into uniform angles matching the SUEM data. Standard deviations of the muscle moment arms from the cadaver study were calculated from source data provided by the study authors. Python code was then used to calculate the differences, percent differences, and root-mean-square (RMS) values between the data sets.

Of the 14 muscle groups in the SUEM, the smallest difference in predicted and measured moment arm was for the supraspinatus during the abduction task, with an RMS of the percent difference of 11.4%. In contrast, the middle latissimus dorsi had an RMS percent difference over 400% during the flexion task. The table presents the RMS difference and the RMS of the percent difference for the muscles with the largest abduction and adduction moment arms (during abduction) and the largest flexion and extension moment arms (during flexion). The moment arm data for the SUEM model and the cadaver data (with 1 standard deviation band) during the motion of the same muscles are provided in Figure 1 for the Abduction motion task and in Figure 2 for the Flexion motion task.

It is challenging to simulate the three dimensional, time variant geometries of shoulder muscles while maintaining model fidelity and optimizing computational cost. Dividing muscles in to sub regions and using wrapping line segment approximations appears a reasonable strategy though more work could improve model accuracy especially during complex three dimensional motions.

Numerous studies have reported on the effects of modular insert design on stress at the tibial/femoral articular surface. However, while the insert / tibial component surface (“backside”) wear and motion have been investigated, backside stress is not well delineated. Because stress may be related to observed backside damage, this study addressed the backside stress response to insert thickness, material, and articular geometry.

Twelve Natural Knee II tibial inserts (Sulzer Orthopedics Inc.) with three thicknesses (6, 12.5, and 18.5 mm), two materials (Durasul and 4150 UHMWPE), and two types of condylar geometry (congruent and ultra-congruent) were tested. Fuji film was placed between the baseplate and insert. A femoral component was loaded onto the insert in axial compression at four times Body Weight. The film was scanned into Adobe Photoshop to measure mean and peak luminosity, which was converted into stress. Analysis of Variance was performed with main effects and all two-way interactions to determine significance.

The mean stress ranged from 0.61 to 3.92 MPa and the peak stress ranged from 2.17 to 10.4 MPa. Insert thickness significantly influenced both mean (p=0.001) and peak (p=0.001) backside stress. Stress for the 6 mm inserts (7.17 MPa mean, 9.91 MPa peak) were approximately 2.1 times the 12.5 mm inserts (3.47 MPa mean, 4.66 MPa peak), and were approximately 2.6 times the 18.5 mm inserts (2.74 MPa mean, 3.71 MPa peak). There was not a significant effect on mean or peak stress from material or condylar geometry. None of the interactions were significant.

This study provides two important contributions. First, it establishes the backside stress magnitude during simple loading. Second, the relationship between backside stress and the insert thickness is experimentally quantified. Understanding this stress magnitude and response may be important to controlling observed in-vivo backside damage.