The paper describes the architecture of a non-invasive, wireless system for fall prevention. The system includes: i) a wearable electroencephalography (EEG) and electromyography (EMG) measurement subsystem that detects the occurrence of unintentional limb movements as sign of a potential fall, and ii) a computing subsystem that classifies EEG-EMG signals, annotates them in a machine-understandable formalism so that semantic-based inferences can recognize real falls, select causes and generate a proper feedback. The overall system is able to prevent a fall enabling the actuator in 168ms, i.e., better than the normal human time reaction (300ms).



Autori: 
De Venuto, Daniela  
Ruta, Michele  
Di Sciascio, Eugenio  
mostra contributor esterni 
Titolo: Designing a Cyber–Physical System for Fall Prevention by Cortico–Muscular Coupling Detection
Rivista: 
IEEE DESIGN & TEST  
Data di pubblicazione: 2016
Digital Object Identifier (DOI): http://dx.doi.org/10.1109/MDAT.2015.2480707
Appare nelle tipologie:1.1 Articolo in rivista

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http://hdl.handle.net/11589/470
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