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Review
. 2019 May 14:2019:7472039.
doi: 10.1155/2019/7472039. eCollection 2019.

Knee Joint Biomechanical Gait Data Classification for Knee Pathology Assessment: A Literature Review

Mariem Abid  1   2 , Neila Mezghani  1   2 , Amar Mitiche  3
Affiliations
Review

Knee Joint Biomechanical Gait Data Classification for Knee Pathology Assessment: A Literature Review

Mariem Abid et al. Appl Bionics Biomech. .

Abstract

Background: The purpose of this study is to review the current literature on knee joint biomechanical gait data analysis for knee pathology classification. The review is prefaced by a presentation of the prerequisite knee joint biomechanics background and a description of biomechanical gait pattern recognition as a diagnostic tool. It is postfaced by discussions that highlight the current research findings and future directions.

Methods: The review is based on a literature search in PubMed, IEEE Xplore, Science Direct, and Google Scholar on April 2019. Inclusion criteria admitted articles, written in either English or French, on knee joint biomechanical gait data classification in general. We recorded the relevant information pertaining to the investigated knee joint pathologies, the participants' attributes, data acquisition, feature extraction, and selection used to represent the data, as well as the classification algorithms and validation of the results.

Results: Thirty-one studies met the inclusion criteria for review.

Conclusions: The review reveals that the importance of medical applications of knee joint biomechanical gait data classification and recent progress in data acquisition technology are fostering intense interest in the subject and giving a strong impetus to research. The review also reveals that biomechanical data during locomotion carry essential information on knee joint conditions to infer an early diagnosis. This survey paper can serve as a useful informative reference for research on the subject.

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Figures

Figure 1
Figure 1
The knee joint coordinate system as defined by Grood and Suntay.
Figure 2
Figure 2
Roadmap of this survey.
Figure 3
Figure 3
Flow of article inclusion/exclusion throughout the review process.
Figure 4
Figure 4
The waveform of the knee flexion angle for a normal subject is shown normalized to 100% of the gait cycle.
Figure 5
Figure 5
An illustration of gait phases.
Figure 6
Figure 6
Illustration of high dimensionality and variability. The graph shows the graph of a sample of 160 distinct asymptomatic abduction/adduction waveforms, each composed of 100 measurement points.
Figure 7
Figure 7
Example of representation of points of interest on kinematic waveforms.
See this image and copyright information in PMC

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