A novel method for bone fatigue monitoring and prediction

Abstract

Bone fatigue, often manifest as stress fractures, is a common injury that plagues many individuals, adversely affect quality of life, and is an obstacle to extended human spaceflight. This manuscript details a pilot study that was conducted to determine if the Phase Space Warping (PSW) methodology could be used to monitor/predict fatigue failure in bone tissue. A Moon's beam experimental apparatus was used to perform variable amplitude fatigue tests on bovine bone specimens. Scanning electron microscopy was used to evaluate the fracture surface and identify the fracture type. The PSW method allowed for successful identification of the various damage modes and may lead to the development of a viable tool for predicting the health and fatigue life of bone.

Geometry-Informed Optimal Parameter Selection for Phase Space Warping with Application to Early Fatigue Damage Detection in 3D-Printed Beams

“…4 , where we observed no significant variations from the obtained sensitivity indices. This selection also ensures that no excessive samples are drawn from the predefined sample space Ω, where three of four parameters are sparse.…”

“…The PSW algorithm is a non-parametric estimator for the slow-time dynamics in the HDS that requires numerous input parameters that determine the algorithm's performance. However, the parameter selection was not explicitly explained or discussed in the available literature apart from providing some basic ranges for the selection [1,3,4,10].…”

“…Since no prior knowledge is available on the distribution of the four factors (as random input variables), uniform distribution for each parameter is assumed. However, since the previous studies proposed valuable ranges for the parameters to consider [1,3,4,10], the distributions are designated as follows nm ∼ U(2 5 , 2 13 )…”

“…The PSW-based algorithms are able to extract or reconstruct the hidden slow-time phase space dynamics from the observable fast-time dynamic measurements [1]. Applications of the PSW ranges from tracking structural degradation [2][3][4], estimating slow-time multivariate parameter drifts in electromechanical systems [5], identifying physiological fatigue and failure detection in biomechanics [6][7][8] to identifying invariant manifolds [9].…”

“…Second, a set of predetermined inputs to the PSW algorithm considerably affect the accuracy and robustness of the estimation. Nevertheless, no corresponding guidelines for the parameter selection were provided in the previous works [1,[3][4][5]10]. Recently, several attempts have been made to improve the damage tracking quality of the PSW method by incorporating more sophisticated algorithms and metrics.…”