Article

Assessing Posture in Surgery: Video Sampling of Microsurgery

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Abstract

Prolonged static work postures and posture constraints imposed by surgical equipment may increase the risk of musculoskeletal pain and discomfort in surgeons. Four surgeons were video recorded performing live microsurgery and a work sampling methodology was used to quantify their upper body postures, investigating the proportion of static and dynamic postures, and compare surgeon postures to the postures found in laboratory studies. Surgeons were found assuming flexed postures in their neck, shoulder, elbow, and back. Most of the procedure consists of static postures where movement greater than 10 degrees per second was not frequently detected in our sampling. Of the four surgeons, one surgeon was found on average to have lower neck flexion than the other three. Further investigation showed the surgeon looking through the microscope at a distance to assume a more upright neutral posture. Identifying surgeon work postures is a key step to understanding how to reduce musculoskeletal pain and discomfort.

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... Posture-matching was conducted using University of Michigan's 3D Static Strength Prediction Program (3DSSPP, version 6.06) as shown in Fig. 2. Although posturematching accuracy may be limited by anthropometric scaling and inverse kinematic algorithms, published strategies for improving posture-matching accuracy were applied for each pull exertion (Beck and Chaffin, 1992;Vi et al., 2000;Liu et al., 1997;Yu et al., 2012). For each photo, the following steps were completed: ...
... (1) Input participant's anthropometry (i.e., gender, height, and body weight) and observed horizontal and vertical pulling force in 3DSSPP (2) Execute 3DSSPP's posture prediction module using inverse kinematics (Beck and Chaffin, 1992) based on known handle heights (3) Refine posture prediction manually by uploading the 2D photo captured for the specific pull exertion in 3DSSPP (Vi et al., 2000), and then scale and rotate the digital human model to match camera distance and participant's position as seen in the photo (4) Input measurable sagittal angles into 3DSSPP, i.e., lower arm, upper arm, upper leg, lower leg, and trunk. Sagittal angles are estimated from 2D photos by marking the pixel location of joint centers and computing the angle formed by these points using MathWorks' Matlab™ software R2012a (Yu et al., 2012), and (5) Adjust and refine the remaining angles using 3DSSPP posture input interface to match 2D photo (Liu et al., 1997) and satisfy biomechanical constraints, e.g., known handle height and center of pressure is within base-of-support. ...
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