Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson’s Disease: Addressing the Class Imbalance Problem
Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN 37996, USA
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Sensors 2019, 19(18), 3898; https://doi.org/10.3390/s19183898
Received: 15 August 2019
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Revised: 5 September 2019
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Accepted: 8 September 2019
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Published: 10 September 2019
(This article belongs to the Special Issue Wearable System-Based Sensors for Ambient Assisted Living)
Freezing of gait (FoG) is a common motor symptom in patients with Parkinson’s disease (PD). FoG impairs gait initiation and walking and increases fall risk. Intelligent external cueing systems implementing FoG detection algorithms have been developed to help patients recover gait after freezing. However, predicting FoG before its occurrence enables preemptive cueing and may prevent FoG. Such prediction remains challenging given the relative infrequency of freezing compared to non-freezing events. In this study, we investigated the ability of individual and ensemble classifiers to predict FoG. We also studied the effect of the ADAptive SYNthetic (ADASYN) sampling algorithm and classification cost on classifier performance. Eighteen PD patients performed a series of daily walking tasks wearing accelerometers on their ankles, with nine experiencing FoG. The ensemble classifier formed by Support Vector Machines, K-Nearest Neighbors, and Multi-Layer Perceptron using bagging techniques demonstrated highest performance (F1 = 90.7) when synthetic FoG samples were added to the training set and class cost was set as twice that of normal gait. The model identified 97.4% of the events, with 66.7% being predicted. This study demonstrates our algorithm’s potential for accurate prediction of gait events and the provision of preventive cueing in spite of limited event frequency.
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Keywords:
ensemble classifier; data synthesis; ADASYN; cost of classification; freezing of gait; Parkinson’s disease; wearable sensors
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MDPI and ACS Style
Naghavi, N.; Miller, A.; Wade, E. Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson’s Disease: Addressing the Class Imbalance Problem. Sensors 2019, 19, 3898. https://doi.org/10.3390/s19183898
AMA Style
Naghavi N, Miller A, Wade E. Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson’s Disease: Addressing the Class Imbalance Problem. Sensors. 2019; 19(18):3898. https://doi.org/10.3390/s19183898
Chicago/Turabian StyleNaghavi, Nader, Aaron Miller, and Eric Wade. 2019. "Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson’s Disease: Addressing the Class Imbalance Problem" Sensors 19, no. 18: 3898. https://doi.org/10.3390/s19183898
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