Campus Units

Mechanical Engineering, Computer Science, Kinesiology

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

4-8-2015

Journal or Book Title

Proceedings of the Royal Society

Volume

A471

Issue

20140526

DOI

10.1098/rspa.2014.0526

Abstract

Individuals who suffer anterior cruciate ligament (ACL) injury are at higher risk of developing knee osteoarthritis (OA) and almost 50% display symptoms 10–20 years post injury. Anterior cruciate ligament reconstruction (ACLR) often does not protect against knee OA development. Accordingly, a multi-scale formulation for data-driven prognosis (DDP) of post-ACLR is developed. Unlike traditional predictive strategies that require controlled off-line measurements or ‘training’ for determination of constitutive parameters to derive the transitional statistics, the proposed DDP algorithm relies solely on in situmeasurements. The proposed DDP scheme is capable of predicting onset of instabilities. As the need for off-line testing (or training) is obviated, it can be easily implemented for ACLR, where such controlled a priori testing is almost impossible to conduct. The DDP algorithm facilitates hierarchical handling of the large dataset and can assess the state of recovery in post-ACLR conditions based on data collected from stair ascent and descent exercises of subjects. The DDP algorithm identifies inefficient knee varus motion and knee rotation as primary difficulties experienced by some of the post-ACLR population. In such cases, levels of energy dissipation rate at the knee, and its fluctuation may be used as measures for assessing progress after ACL reconstruction.

Comments

This accepted manuscript is from an article published as Chandra A, Kar O, Wu K, Hall M & Gillette JC. (2015). Prognosis of anterior cruciate ligament (ACL) reconstruction: A data driven approach. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 471:20140526. DOI: 10.1098/rspa.2014.0526. Posted with permission.

Copyright Owner

The Author(s) et al

Language

en

File Format

application/pdf

Published Version

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