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Article

Tracking Foot Drop Recovery Following Lumbar-Spine Surgery, Applying Multiclass Gait Classification Using Machine Learning Techniques

1
School of Civil and Mechanical Engineering, Curtin University of Technology, Perth 6102, Australia
2
School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University of Technology, Perth 6102, Australia
3
St John of God Subiaco Hospital Perth 6008, Australia and School of Surgery of University of Western Australia, Perth 6009, Australia
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(11), 2542; https://doi.org/10.3390/s19112542
Received: 14 February 2019 / Revised: 20 May 2019 / Accepted: 29 May 2019 / Published: 4 June 2019
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
The ability to accurately perform human gait evaluation is critical for orthopedic foot and ankle surgeons in tracking the recovery process of their patients. The assessment of gait in an objective and accurate manner can lead to improvement in diagnoses, treatments, and recovery. Currently, visual inspection is the most common clinical method for evaluating the gait, but this method can be subjective and inaccurate. The aim of this study is to evaluate the foot drop condition in an accurate and clinically applicable manner. The gait data were collected from 56 patients suffering from foot drop with L5 origin gathered via a system based on inertial measurement unit sensors at different stages of surgical treatment. Various machine learning (ML) algorithms were applied to categorize the data into specific groups associated with the recovery stages. The results revealed that the random forest algorithm performed best out of the selected ML algorithms, with an overall 84.89% classification accuracy and 0.3785 mean absolute error for regression. View Full-Text
Keywords: foot drop; gait classification; machine learning; inertial measurement unit foot drop; gait classification; machine learning; inertial measurement unit
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MDPI and ACS Style

Sharif Bidabadi, S.; Tan, T.; Murray, I.; Lee, G. Tracking Foot Drop Recovery Following Lumbar-Spine Surgery, Applying Multiclass Gait Classification Using Machine Learning Techniques. Sensors 2019, 19, 2542. https://doi.org/10.3390/s19112542

AMA Style

Sharif Bidabadi S, Tan T, Murray I, Lee G. Tracking Foot Drop Recovery Following Lumbar-Spine Surgery, Applying Multiclass Gait Classification Using Machine Learning Techniques. Sensors. 2019; 19(11):2542. https://doi.org/10.3390/s19112542

Chicago/Turabian Style

Sharif Bidabadi, Shiva, Tele Tan, Iain Murray, and Gabriel Lee. 2019. "Tracking Foot Drop Recovery Following Lumbar-Spine Surgery, Applying Multiclass Gait Classification Using Machine Learning Techniques" Sensors 19, no. 11: 2542. https://doi.org/10.3390/s19112542

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