FPGA Based Architecture for Fall-risk Assessment During Gait Monitoring by Synchronous EEG/EMG

Annese, V.F. and De Venuto, D. (2015) FPGA Based Architecture for Fall-risk Assessment During Gait Monitoring by Synchronous EEG/EMG. In: 2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI), Gallipoli, Italy, 18-19 Jun 2015, pp. 116-121. ISBN 9781479989812 (doi: 10.1109/IWASI.2015.7184953)

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Abstract

One out of three subjects older than 65 years falls. Despite extensive research, existing assessment tools for fall risk have been insufficient for predicting falls since the phenomenology is complex and there is no equipment on the market that allows everyday life monitoring. In this paper we present a novel approach for fall-risk on-line assessment based on: i) clinical condition of the subject, ii) environmental conditions, iii) electromyographic (EMG) co-contraction analysis and iv) electroencephalographic (EEG) analysis based on Movement Related Potentials (MRPs) and μ-rhythm event related desynchronizations (μ-ERDs) occurrence. This fall-risk assessment approach is implemented by a complete cyber-physical system made up by EEG and EMG wearable recording systems interfaced to an FPGA on-line performing the needed real-time processing for indexes extraction. The results present a fall-risk assessment case study on healthy subjects walking showing detectable fall-risk increasing (+1.5%) when obstacles are overcome.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Annese, Dr Valerio
Authors: Annese, V.F., and De Venuto, D.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
ISBN:9781479989812
Published Online:13 August 2015

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