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Assessment of gait complexity in Parkinson's disease through a network of wireless wearable sensors

Abstract:

The present work describes a methodology that will serve as the base for a human gait analysis module for the assessment of Parkinson's disease (PD). The module is composed by a network of five wireless accelerometers located on the limbs and trunk. The current work shows that it is possible to discriminate the complexity of the gait patterns between PD patients and healthy subjects using the sample entropy (SE) technique. It also shows that increases in entropy are related to the Unified Parkinson's Disease Rating Scale (UPDRS) severity of gait, although this relationship is not linear. © ICT&S Center, University of Salzburg.