Gibbons, Andrew; Clingo, Kelly; Emmett, Darian; Fullwood, David; Bowden, Anton (Brigham Young University)
Faculty Advisor: Fullwood, David (Brigham Young University, Ira A. Fulton College of Engineering; Engineering and Technology); Bowden, Anton (Brigham Young University, Ira A. Fulton College of Engineering; Engineering and Technology)
Spine dysfunctions such as stenosis and herniated discs have traditionally been diagnosed using X-ray or MRI imaging techniques; but these methods capture a snapshot of the problem, without revealing the positional dependence of the causes and effects. In order to provide a richer dataset to physicians, an NIH-funded project has begun with the aim of tracking details of spinal motion for people with healthy and symptomatic backs. Novel nanocomposite strain gauges will be used to capture skin deformation during typical back motion, and correlate these data with back motions that are known to reveal chronic subcutaneous trauma. This paper focuses on the optimal placement of strain gauges for maximum resolution of the underlying biomechanics.
An array of reflective markers was placed on a healthy individual's lower back between the L5 and T10 vertebrae. A QUALISYS motion capture lab was then used to determine the coordinates of these markers during flexion, rotation, flexion with rotation, and side bending. These motions were repeated 3 times for 10 seconds. The distances between markers were calculated for each motion and the strain values between resting and flexed positions were determined. Initial validation was performed by comparing a maximum tensile strain of 0.54, between the L5 and L1 vertebrae in flexion, with a previously reported value of 0.5 in the literature.
This paper will report the development of an optimal arrangement of sensors for resolving the relevant biomechanics of the spine, based upon a detailed analysis of the optical marker results. Future work will utilize these results to develop a skin mounted, wearable sensor array that can measure the real-time kinematics of the spine and compare them with a database of healthy and low back pain subjects using a machine-learning paradigm. We hope to use the system to identify mechanical sources of low-back pain.