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).
Designing a Cyber–Physical System for Fall Prevention by Cortico–Muscular Coupling Detection / De Venuto, Daniela; Annese, Valerio F.; Ruta, Michele; Di Sciascio, Eugenio; Sangiovanni V, Alberto L.. - In: IEEE DESIGN & TEST. - ISSN 2168-2356. - STAMPA. - 33:3(2016), pp. 66-76. [10.1109/MDAT.2015.2480707]
Designing a Cyber–Physical System for Fall Prevention by Cortico–Muscular Coupling Detection
De Venuto, Daniela
;Ruta, Michele;Di Sciascio, Eugenio;
2016-01-01
Abstract
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).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
