<abstract xmlns="http://www.w3.org/1999/xhtml">

<italic>Study aim:</italic> To find out, without relying on gait-specific assumptions or prior knowledge, which parameters are most important for the description of asymmetrical gait in patients after total hip arthroplasty (THA).
<italic>Material and methods:</italic> The gait of 22 patients after THA was recorded using an optical motion capture system. The waveform data of the marker positions, velocities, and accelerations, as well as joint and segment angles, were used as initial features. The random forest (RF) and minimum-redundancy maximum-relevance (mRMR) algorithms were chosen for feature selection. The results were compared with those obtained from the use of different dimensionality reduction methods.
<italic>Results:</italic> Hip movement in the sagittal plane, knee kinematics in the frontal and sagittal planes, marker position data of the anterior and posterior superior iliac spine, and acceleration data for markers placed at the proximal end of the fibula are highly important for classification (accuracy: 91.09%). With feature selection, better results were obtained compared to dimensionality reduction.
<italic>Conclusion:</italic> The proposed approaches can be used to identify and individually address abnormal gait patterns during the rehabilitation process via waveform data. The results indicate that position and acceleration data also provide significant information for this task.
</abstract>

Study aim: To find out, without relying on gait-specific assumptions or prior knowledge, which parameters are most important for the description of asymmetrical gait in patients after total hip arthroplasty (THA).
Material and methods: The gait of 22 patients after THA was recorded using an optical motion capture system. The waveform data of the marker positions, velocities, and accelerations, as well as joint and segment angles, were used as initial features. The random forest (RF) and minimum-redundancy maximum-relevance (mRMR) algorithms were chosen for feature selection. The results were compared with those obtained from the use of different dimensionality reduction methods.
Results: Hip movement in the sagittal plane, knee kinematics in the frontal and sagittal planes, marker position data of the anterior and posterior superior iliac spine, and acceleration data for markers placed at the proximal end of the fibula are highly important for classification (accuracy: 91.09%). With feature selection, better results were obtained compared to dimensionality reduction.
Conclusion: The proposed approaches can be used to identify and individually address abnormal gait patterns during the rehabilitation process via waveform data. The results indicate that position and acceleration data also provide significant information for this task.

Feature extraction and gait classification in hip replacement patients on the basis of kinematic waveform data

Department Augmented Vision, German Research Center for Artificial Intelligence (DFKI)

Junior Research Group wearHEALTH, Technische Universität Kaiserslautern

Biomedical Human Kinetics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Study aim: To find out, without relying on gait-specific assumptions or prior knowledge, which parameters are most important for the description of...

Feature extraction and gait classification in hip replacement patients on the basis of kinematic waveform data

Pubblicato online: 04 giu 2021

Pagine: 177 - 186

Ricevuto: 22 feb 2021

Accettato: 16 apr 2021

DOI: https://doi.org/10.2478/bhk-2021-0022

Parole chiave
Classification, Total hip arthroplasty, Feature selection, Dimensionality reduction

© 2021 Carlo Dindorf et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Feature extraction and gait classification in hip replacement patients on the basis of kinematic waveform data

Pubblicato online: 04 giu 2021

Pagine: 177 - 186

Ricevuto: 22 feb 2021

Accettato: 16 apr 2021

DOI: https://doi.org/10.2478/bhk-2021-0022

Parole chiaveClassification, Total hip arthroplasty, Feature selection, Dimensionality reduction

© 2021 Carlo Dindorf et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
Classification, Total hip arthroplasty, Feature selection, Dimensionality reduction