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).
|Titolo:||Designing a Cyber–Physical System for Fall Prevention by Cortico–Muscular Coupling Detection|
|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|