The truth machine of involuntary movement: FPGA based cortico-muscular analysis for fall prevention

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.

[1]  Dimitrios Makris,et al.  Fall detection system using Kinect’s infrared sensor , 2014, Journal of Real-Time Image Processing.

[2]  Alberto L. Sangiovanni-Vincentelli,et al.  Designing a Cyber–Physical System for Fall Prevention by Cortico–Muscular Coupling Detection , 2016, IEEE Design & Test.

[3]  Gang Zhou,et al.  Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.

[4]  Daniela De Venuto,et al.  Fall-risk assessment by combined movement related potentials and co-contraction index monitoring , 2015, 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[5]  Daniela De Venuto,et al.  Combined EEG/EMG evaluation during a novel dual task paradigm for gait analysis , 2015, 2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI).

[6]  Daniela De Venuto,et al.  FPGA based architecture for fall-risk assessment during gait monitoring by synchronous EEG/EMG , 2015, 2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI).

[7]  Daniela De Venuto,et al.  Gait analysis for fall prediction using EMG triggered movement related potentials , 2015, 2015 10th International Conference on Design & Technology of Integrated Systems in Nanoscale Era (DTIS).

[8]  Joav Merrick,et al.  Neurological Disorders: Public Health Challenges , 2007 .

[9]  Eleonora Vecchio,et al.  Combining EEG and EMG Signals in a Wireless System for Preventing Fall in Neurodegenerative Diseases , 2015 .

[10]  D. Oliver,et al.  Falls risk-prediction tools for hospital inpatients. Time to put them to bed? , 2008, Age and ageing.

[11]  S. D. de Rooij,et al.  Fear of falling: measurement strategy, prevalence, risk factors and consequences among older persons. , 2008, Age and ageing.

[12]  Y. Lajoie,et al.  Predicting falls within the elderly community: comparison of postural sway, reaction time, the Berg balance scale and the Activities-specific Balance Confidence (ABC) scale for comparing fallers and non-fallers. , 2004, Archives of gerontology and geriatrics.

[13]  Israel Gannot,et al.  A Method for Automatic Fall Detection of Elderly People Using Floor Vibrations and Sound—Proof of Concept on Human Mimicking Doll Falls , 2009, IEEE Transactions on Biomedical Engineering.