Abstract
In this paper we propose a novel approach for online fall-risk assessment based on concurrent EEG and EMG monitoring. The fall-risk evaluation is based on: i) clinical condition of the individual, ii) environment, iii) EMG agonist-Antagonist co-contraction analysis and iv) Movement Related Potentials and event related desynchronizations occurrence/absence. The fall-risk assessment evaluation algorithm has been implemented on a FPGA (Altera Cyclone V SE 5CSEMA5F31C6N) in order to realize an autonomous and stand-Alone fall prevention tool. The experimental results (based on a dataset of 10 individuals) are described and demonstrate the validity of the algorithm and its FPGA implementation, which responds in 41ms, well within the 300ms time limit according to a study on 45 fallers and 80 non-fallers (with 74 years average age). © 2015 IEEE.