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