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• W2917698049 endingPage "948" @default.
• W2917698049 startingPage "948" @default.
• W2917698049 abstract "We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson’s disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability. The targets of this work are detecting the FOG episodes, distinguishing the phenotype and analyzing the muscle activity during and outside FOG, toward a deeper insight in the disorder pathophysiology and the assessment of the fall risk associated to the FOG subtype. To this aim, gyroscopes and surface electromyography integrated in wearable devices sense simultaneously movements and action potentials of antagonist leg muscles. Dedicated algorithms allow the timely detection of the FOG episode and, for the first time, the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype. Thanks to the possibility of detecting muscles contractions and stretching exactly during FOG, a deeper insight into the pathophysiological underpinnings of the different phenotypes can be achieved, which is an innovative approach with respect to the state of art." @default.
• W2917698049 created "2019-03-02" @default.
• W2917698049 creator A5019326706 @default.
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• W2917698049 creator A5066215505 @default.
• W2917698049 date "2019-02-23" @default.
• W2917698049 modified "2023-10-01" @default.
• W2917698049 title "Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial Signals" @default.
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• W2917698049 doi "https://doi.org/10.3390/s19040948" @default.
• W2917698049 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6412484" @default.
• W2917698049 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30813411" @default.
• W2917698049 hasPublicationYear "2019" @default.
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