Abstract

Fall events are one of the main causes of injuries among the elderly. The purpose of this study has been to identify a computational framework for the real-time and automatic detection of the fall risk, allowing the fast adoption of properly intervention strategies, to reduce injuries and traumas due to the fall. A wearable, wireless and minimally invasive surface Electromyography (EMG)-based system has been used to measure four lower-limb muscles activities. Eleven young healthy subjects have simulated several fall events (through a movable platform) and normal Activities of Daily Living (ADLs) and their patterns have been analysed. Highly discriminative features extracted within the EMG signals for the pre impact fall evaluation have been explored and a threshold-based approach has been adopted, assuring the real-time functioning. The threshold level for each feature has been set to distinguish an instability condition from normal activities. The proposed system seems able to recognize all falls with an average lead-time of 840ms before the impact, in simulated and controlled fall conditions.

Autore Pugliese

• Leone Alessandro , Rescio Gabriele

Tutti gli autori

• Leone A.; Rescio G.; Caroppo A.; Siciliano P.; Casino F.

Titolo volume/Rivista

International journal of engineering and technology innovation

Anno di pubblicazione

2015

ISSN

2223-5329

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

Non Disponibile

Ultimo Aggiornamento Citazioni

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Settori ERC

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Codici ASJC

Non Disponibile