2023
DOI: 10.1109/tnsre.2022.3231883
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Bilateral Leg Stepping Coherence as a Predictor of Freezing of Gait in Patients With Parkinson’s Disease Walking With Wearable Sensors

Abstract: Freezing of Gait (FOG) is among the most debilitating symptoms of Parkinson's Disease (PD), characterized by a sudden inability to generate effective stepping. In preparation for the development of a real-time FOG prediction and intervention device, this work presents a novel FOG prediction algorithm based on detection of altered interlimb coordination of the legs, as measured using two inertial movement sensors and analyzed using a wavelet coherence algorithm. Methods: Fourteen participants with PD (in OFF st… Show more

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Cited by 7 publications
(1 citation statement)
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“…Although a previous study proposed using physiological signals, such as electrocardiography, to detect discriminative features for classifying FOG from voluntary stops [ 45 ], methods using motor signals to distinguish FOG from stops were seldom investigated. To the best of our knowledge, only limited studies proposed FOG detection or prediction on trials with stops using IMU signals [ 31 , 46 ]. However, while these studies developed models to detect FOG from data that contains voluntary stopping, they did not address the effect of including or excluding stopping instances during the model training phase on FOG detection performance, forming the fourth research gap.…”
Section: Introductionmentioning
confidence: 99%
“…Although a previous study proposed using physiological signals, such as electrocardiography, to detect discriminative features for classifying FOG from voluntary stops [ 45 ], methods using motor signals to distinguish FOG from stops were seldom investigated. To the best of our knowledge, only limited studies proposed FOG detection or prediction on trials with stops using IMU signals [ 31 , 46 ]. However, while these studies developed models to detect FOG from data that contains voluntary stopping, they did not address the effect of including or excluding stopping instances during the model training phase on FOG detection performance, forming the fourth research gap.…”
Section: Introductionmentioning
confidence: 99%