Classification of foot drop gait characteristic due to lumbar radiculopathy using machine learning algorithms
MetadataShow full item record
© 2019 Elsevier B.V. Background: Recently, the study of walking gait has received significant attention due to the importance of identifying disorders relating to gait patterns. Characterisation and classification of different common gait disorders such as foot drop in an effective and accurate manner can lead to improved diagnosis, prognosis assessment, and treatment. However, currently visual inspection is the main clinical method to evaluate gait disorders, which is reliant on the subjectivity of the observer, leading to inaccuracies. Research question: This study examines if it is feasible to use commercial off-the-shelf Inertial measurement unit sensors and supervised learning methods to distinguish foot drop gait disorder from the normal walking gait pattern. Method: The gait data collected from 56 adults diagnosed with foot drop due to L5 lumbar radiculopathy (with MRI verified compressive pathology), and 30 adults with normal gait during multiple walking trials on a flat surface. Machine learning algorithms were applied to the inertial sensor data to investigate the feasibility of classifying foot drop disorder. Results: The best three performing results were 88.45%, 86.87% and 86.08% accuracy derived from the Random Forest, SVM, and Naive Bayes classifiers respectively. After applying the wrapper feature selection technique, the top performance was from the Random Forest classifier with an overall accuracy of 93.18%. Significance: It is demonstrated that the combination of inertial sensors and machine learning algorithms, provides a promising and feasible solution to differentiating L5 radiculopathy related foot drop from normal walking gait patterns. The implication of this finding is to provide an objective method to help clinical decision making.
Showing items related by title, author, creator and subject.
Bidabadi, S.; Murray, Iain; Lee, G. (2018)© 2017 IEEE. Foot drop is a frequent gait disorder which is challenging to identify. Currently, subjective methods are being applied for detection of foot drop in clinical applications. This research introduces an objective ...
Validation of foot pitch angle estimation using inertial measurement unit against marker-based optical 3D motion capture systemSharif Bidabadi, S.; Murray, Iain; Lee, G. (2018)Gait analysis is relevant to a broad range of clinical applications in areas of orthopedics, neurosurgery, rehabilitation and the sports medicine. There are various methods available for capturing and analyzing the gait ...
The application of inertial measurements unit for the clinical evaluation and assessment of gait eventsSharif Bidabadi, S.; Murray, Iain; Lee, G. (2017)© 2017 Informa UK Limited, trading as Taylor & Francis Group Foot drop is one of the common gait abnormalities which are difficult to detect, diagnose and evaluate. While various gait monitoring systems are available, ...