1. Variation in Shoulder Elevation
Estimating Upper Extremity Joint Contributions in Functional Motions to Create a Metric for Injury Prevention using OpenSim
Eric Honert; Fernando Aguilar; Alexander Kozlowski; Craig M. Goehler, PhD
Results
Introduction
The resulting ranges of motion for shoulder elevation and elbow flexion for both the drinking and pointing
tasks were as expected, agreeing with the initial hypothesis and prior literature [1].
All of the subjects displayed significantly different (p<0.05) average joint configurations between drinking and pointing tasks at both the shoulder and elbow joints.
Nine out of ten subjects displayed a significant difference (p<0.05) in the variation in joint configuration for the shoulder between the two tasks; eight out of ten subjects displayed a significant difference (p<0.05) in the variation in joint configuration for the elbow between the two tasks.
When combining all trials for all subjects into one common group, there is a significant difference (p<0.05) in the average joint configuration for the shoulder and the elbow between both tasks and in the variation in joint configuration for the shoulder and the elbow between both tasks (illustrated in Table).
For the drinking task, there is a significant difference (p<0.05) in the average joint configuration and variation in joint configuration for the shoulder joint between males and females.
For the pointing task, there is a significant difference (p<0.05) in the average joint configuration for the elbow joint between males and females.
A multitude of tasks are performed everyday with minimal direct thought on how to orient the arm.
Little research has been reported in the area of how individual joint contributions vary between different upper extremity tasks.
In order to quantify individual joint contributions of complex spatial arm movements, it is beneficial to examine recorded arm movements within a simulation environment such as OpenSim [2].
The aim of this project is to compare selected motions using the results of kinematic and dynamic simulations performed in OpenSim [4].
Functional movements have the potential to predict risk of injury based on their frequency.
A metric can be established using a variety of healthy subjects performing the motions of interest.
Two tasks were initially selected based on qualitatively observed differences in shoulder and elbow joint contributions: drinking and pointing/reaching.
It was hypothesized that motions covering a large three-dimensional workspace will require greater contributions from the shoulder joint, while motions with more of a planar workspace will rely more heavily on the elbow joint.
Drinking
Pointing
Average
Shoulder Elevation
46.98° (8.44°)
75.54° (3.11°)
Variation in Shoulder Elevation
6.32° (3.68°)
13.58° (2.51°)
Elbow Flexion
77.97° (11.38°)
15.72° (6.91°)
Variation in
15.14° (5.39°)
9.46° (3.51°)
A range of functional movements will be recorded and analyzed to develop quantitative metrics that will serve as indicators for potential injuries.
Inform training protocols aimed at reducing the overall risk of injuries for athletes.
EMG sensors to quantify the muscle contribution to the motions.
Future Work
Methods
Ten subjects (five male, five female) participated in the IRB approved study.
Reflective markers were placed on the subject’s upper extremity [3].
VICON Nexus software was used to develop a subject-specific model based on a generic template.
The markers on this subject-specific model were anatomically labeled based on the labels used in the original model.
Subject performed five trials for each of the two selected common upper extremity tasks.
The data were processed and filtered in the VICON Nexus software.
The processed files were then filtered and exported as ASCII files for further analysis in OpenSim.
The inverse kinematic results were utilized to determine the joint angles during each motion, and the inverse dynamic results were used to find the joint moments exerted during each motion.
The data were further processed in MATLAB, normalized to a time of ten seconds for comparison across trials, and plotted versus the normalized time.
The mean and the standard deviation of the shoulder elevation and elbow flexion coordinates over the entire trial were calculated.
References
1. Van Andel C, et al. 27 (1), , 2008.
2. Delp S, et al. IEEE Trans BME 54 (11),
, 2007.
3. Saul K, et al. ASME 2012 SBC.
4. Honert E, et al. ASME 2013 SBC.