Abstract
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.
Highlights
Parkinson’s disease (PD) is clinically characterized by both motor and non–motor symptoms.The most common motor symptoms are slowness of movement, hastening of the gait, paucity of spontaneous movements, and poor postural stability.Gait impairment is the most incapacitating symptom among patients with PD [1], as it negatively affects mobility and independence and results in fall-related injuries, emotional stresses, and deterioration of patients’ quality of life [2,3,4,5].Freezing of gait (FoG) is commonly regarded as a feature of akinesia, an extreme form of bradykinesia [6]
We studied the effect of the ADAptive SYNthetic (ADASYN) sampling algorithm and classification cost on classifier performance
The results show that using data synthesis and increased cost of misclassification, improved sensitivity of ClsfBagging from 85.2% to 90.8%, while keeping the specificity almost untouched, which resulted in improved F1 from 87.7% to 90.7%
Summary
The most common motor symptoms are slowness of movement (bradykinesia), hastening of the gait (festination), paucity of spontaneous movements (akinesia), and poor postural stability. Freezing of gait (FoG) is commonly regarded as a feature of akinesia, an extreme form of bradykinesia [6]. FoG is highly affected by environmental stimuli, cognitive input, medication, and anxiety [8,9]. It occurs more frequently at home than in the clinic, in complete darkness, and in other settings that require greater cognitive load like dual-tasking situations [10,11,12,13]
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