The Truth Machine of Involuntary Movement: FPGA Based Cortico-muscular Analysis for Fall Prevention

Annese, V. F. and De Venuto, D. (2016) The Truth Machine of Involuntary Movement: FPGA Based Cortico-muscular Analysis for Fall Prevention. In: 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Abu Dhabi, United Arab Emirates, 7-10 Dec 2015, ISBN 9781509004812 (doi: 10.1109/ISSPIT.2015.7394398)

Abstract

Voluntary movements are managed by movement related potentials (MRPs) which are brain activity patterns detectable even 500ms before the movement itself. The cortico-muscular matching between brain (EEG) and muscles (EMG) activity allows the assessment of the intentionality of the performed movement. Basing on this knowledge, a real-time algorithm for falling risk prediction based on EMG/EEG coupled analysis is presented. The system architecture involves 8 EMG (limbs) and 8 EEG (motor-cortex) channels wirelessly collected by a FPGA (gateway) that contextually performs the real-time processing based on an event triggered time-frequency approach. The digital architecture is validated on the FPGA to determine resources utilization, related timing constraints and performance figures of a dedicated real-time ASIC implementation for wearable applications. The system resource utilization is 85.95% ALMs, 43283 ALUTs, 73.0% registers, 9.9% block memory of an Altera Cyclone V FPGA. The processing latency is lower than 1ms and the output are available in 56ms, respecting the time limit of 300ms. Outputs enables decision-taking for feedback delivering.

• 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
• Journal Name: 2015 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2015
• ISBN: 9781509004812