Data Reduction and Processing

Three-dimensional coordinates of the digitized bony landmarks were calculated

using the Motion Monitor® software (Innovative Sports Training, Inc. Chicago, IL).

Segment reference frames were defined according to the recommendations set forth by the Shoulder Group of the International Society of Biomechanics.(Wu, van der Helm et al. 2005) Humeral motions were calculated as the Euler angles of the humerus relative to the thorax reference frame in the following order of rotations: internal-external rotation about Y axis, elevation about the Z’ axis, and internal-external rotation about the Y” axis(An, Browne et al. 1991). Scapular motions were calculated as the Euler angles of the scapula relative to the thorax reference frames in the following order of rotations: internal/external rotation about the Y axis, upward-downward rotation about the Z’ axis, and posterior-anterior tilting about the X” axis(Karduna, McClure et al. 2000; Wu, van

der Helm et al. 2005). Kinematic data were smoothed through a Butterworth a low pass

digital-filter (4th order, recursive, zero phase lag) at an estimated optimum cutoff

frequency of 3.5 Hz. The estimated optimum cutoff was determined after performing a spectral analysis for each kinematic variable. All humeral and scapular rotation spectral plots were similar (Figure 8).

3.3.2 EMG Data

Post-acquisition, all EMG data was band-pass filtered (10 – 350 Hz) using a

Butterworth filter (4th order, recursive, zero-phase lag). The data was rectified then the

root mean square error (RMS) of the EMG signal over a 20 ms time constant was taken to further smooth the data.

3.4 Data Processing

3.4.1 Reduction for Scapular Kinematic and Electromyographic Analyses

The average of trials 2-6 of each task were used for assessment of mean scapular angles. Scapular upward rotation, internal rotation, and posterior tilting angles were selected using custom Matlab (Mathworks, Natick, MA) code to identify angles at 60°, 90°, and 120° of humeral elevation during the ascending and descending phases of the forward flexion task. Scapular angles were identified at 60°, 90°, and 110° of the forward reaching task. Each of the scapular and humeral kinematic variables demonstrated

excellent reliability with ICC(2,1) values ranging from 0.92 to 0.99. Scapular ranges of

motion (ROM) were calculated from 30° to 120° of humeral elevation for the ascending and descending phases of humeral elevation.

Mean amplitude EMG was used to represent muscle activation over the ascending and descending phases of humeral elevation for the upper trapezius, lower trapezius, and

serratus anterior. These were the same arcs used to calculate the scapular ROM for the ascending and descending phases of shoulder motion. The mean amplitude EMG over each phase of motion was averaged across each of the 5 trials and used for statistical

analyses. Each of the scapular EMG variables demonstrated good reliability with ICC(2,1)

values ranging from 0.68 to 0.85.

3.4.2 Reduction for Coordination Analyses

The humeral and scapular kinematic data was analyzed using custom Matlab code to calculate mean relative phase (MARP) and deviation phase (DP) values. Shoulder kinematic data was further smoothed using a cubic spline routine. The tolerance of the spline routine was set at .7 where 0 is a perfect least squares fit between the first and last point of the data sequence, and 1 is the natural spline.

Shoulder kinematic data then was fit to 101 points for 15 repetitions for humeral elevation and each scapular motion. Only 15 repetitions were selected based on the potential effects of fatigue. This was supported by a significant increase in PRE values

from 13 at the 15th repetition, to 15 at the 20th repetition, to 17 at the 25th repetition.

Angular position and angular velocity was plotted to create phase portraits for humeral elevation, scapular upward rotation, internal rotation, and posterior tipping. The relative phase was calculated between humeral elevation and each scapular motion. Relative phase for a given segment was calculated from the phase angle of each phase portrait.

The phase portrait path was transformed from Cartesian (x,y) to polar (r,θ) with a radius r

and a phase angle θ. The angle formed by the radius was calculated as: (Kurz and

Stergiou 2004)

⎟

⎞

⎜

⎛Yi

The angle formed between the horizontal and r for each point i was the phase angle. The relative phase angle between each of the scapular rotations and humeral elevation was then calculated as the difference between the proximal segment’s phase angle and the distal segment’s phase angle:(Kurz and Stergiou 2004)

Φ

= θ

–θ

Calculation of mean absolute relative phase (MARP) and deviation phase (DP) allowed for statistical comparison of differences between relative phases. Each continuous relative phase curve was quantified in one term, the mean absolute relative phase (MARP). This value reflects whether the oscillating segments are in or out of phase during a movement cycle. The MARP value was calculated by averaging the absolute value of all the points of the mean ensemble curve.(Kurz and Stergiou 2004)

MARP =

∑

Additionally, the deviation phase (DP) was calculated to determine the variation over the entire relative phase curve. This value is reflective of the stability of the

neuromuscular system during a movement pattern. DP was calculated by averaging the standard deviations (SD) of all the points over the entire mean ensemble curve.(Kurz and Stergiou 2004) DP = N SDi N i

∑

The mean ensemble curve is the curve generated by averaging all single cycle relative phase curves. This required normalization of all single cycle relative phase curves to a fixed number of points (i.e., 101) for each task.

3.5 Statistical Analysis