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A new machine learning based approach to predict Freezing of Gait

Kleanthous, N, Hussain, A, Khan, W and Liatsis, P (2020) A new machine learning based approach to predict Freezing of Gait. Pattern Recognition Letters, 140. pp. 119-126. ISSN 0167-8655

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Freezing of gait (FoG) is a motor symptom of Parkinson’s disease (PD) that frequently occurs in the long-term sufferers of the disease. FoG may result to nursing home admission as it can lead to falls, and therefore, it impacts negatively on the quality of life. The focus of this study is the systematic evaluation of machine learning techniques in conjunction with varying size time windows and time/frequency domain feature sets in predicting a FoG event before its onset. In the experiments, the Daphnet FoG dataset is used to benchmark performance. This consists of accelerometer signals obtained from sensors mounted on the ankle, thigh and trunk of the PD patients. The dataset is annotated with instances of normal activity events, and FoG events. To predict the onset of FoG, the dataset is augmented with an additional class, termed ‘transition’, which relates to a manually defined period prior to the occurrence of a FoG episode. In this research, five machine learning models are used, namely, Random Forest, Extreme Gradient Boosting, Gradient Boosting, Support Vector Machines using Radial Basis Functions, and Neural Networks. Support Vector Machines with Radial Basis kernels provided the best performance achieving sensitivity values of 72.34%, 91.49%, 75.00%, and specificity values of 87.36%, 88.51% and 93.62%, for the FoG, transition and normal activity classes, respectively

Item Type: Article
Uncontrolled Keywords: 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
R Medicine > R Medicine (General)
Divisions: Computer Science & Mathematics
Publisher: Elsevier
Date Deposited: 21 Sep 2020 09:12
Last Modified: 17 Sep 2021 00:50
DOI or ID number: 10.1016/j.patrec.2020.09.011
URI: https://researchonline.ljmu.ac.uk/id/eprint/13670
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