Very low power event-based surface EMG acquisition system with off-the-shelf components

An acquisition system exploiting event-based approach applied to surface ElectroMyoGraphy (sEMG) was developed, with the goal of reducing the device size and minimizing the power consumption. The device has a modular four-channels architecture; each single acquisition module is characterized by a power consumption of 0.635 mW, a body area of 414 mm2 and a weight of 2 g, making it a good fit for wearable applications. In-vivo experiments have been conducted on 11 subjects, during isometric contraction with increasing load of the Biceps Brachii and Vastus Lateralis muscles. The device was able to discriminate different muscular activation levels, through the average number (over time) of threshold-crossing events, providing promising results in terms of correlation with the weight (median values of 0.97 and 0.95 respectively), compared to standard sEMG parameters described in literature.

[1]  Markus Nowak,et al.  Low-cost wearable multichannel surface EMG acquisition for prosthetic hand control , 2015, 2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI).

[2]  Marco Crepaldi,et al.  A hybrid quasi-digital/neuromorphic architecture for tactile sensing in humanoid robots , 2015, 2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI).

[3]  Liqiong Tang,et al.  Surface EMG Signal Amplification and Filtering , 2013 .

[4]  Giorgio Biagetti,et al.  Wireless surface electromyograph and electrocardiograph system on 802.15.4 , 2016, IEEE Transactions on Consumer Electronics.

[5]  C. Pattichis,et al.  Surface EMG analysis on normal subjects based on isometric voluntary contraction. , 2009, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[6]  Huosheng Hu,et al.  The Usefulness of Mean and Median Frequencies in Electromyography Analysis , 2012 .

[7]  M. Khezri,et al.  A Novel Approach to Recognize Hand Movements Via sEMG Patterns , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Marco Crepaldi,et al.  Low power wireless ultra-wide band transmission of bio-signals , 2014 .

[9]  G. Melchiorri,et al.  A method for positioning electrodes during surface EMG recordings in lower limb muscles , 2004, Journal of Neuroscience Methods.

[10]  T. Fukuda,et al.  Root Mean Square Value of the Electromyographic Signal in the Isometric Torque of the Quadriceps, Hamstrings and Brachial Biceps Muscles in Female Subjects , 2010 .

[11]  Marco Crepaldi,et al.  An Analog-Mode Impulse Radio System for Ultra-Low Power Short-Range Audio Streaming , 2015, IEEE Transactions on Circuits and Systems I: Regular Papers.

[12]  Benoit Gosselin,et al.  A Low-power wireless multi-channel surface EMG sensor with simplified ADPCM data compression , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[13]  Ramon Pallàs-Areny,et al.  AC-coupled front-end for biopotential measurements , 2003, IEEE Transactions on Biomedical Engineering.

[14]  Marco Crepaldi,et al.  Wireless Multi-channel Quasi-digital Tactile Sensing Glove-Based System , 2013, 2013 Euromicro Conference on Digital System Design.

[15]  Michele Magno,et al.  Wearable low power dry surface wireless sensor node for healthcare monitoring application , 2013, 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).

[16]  Marco Crepaldi,et al.  On Integration and Validation of a Very Low Complexity ATC UWB System for Muscle Force Transmission , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[17]  Mikhail Kuznetsov,et al.  Filtering the surface EMG signal: Movement artifact and baseline noise contamination. , 2010, Journal of biomechanics.

[18]  Sheroz Khan,et al.  High Quality Acquisition of Surface Electromyography – Conditioning Circuit Design , 2013 .

[19]  R. Shalaby,et al.  Development of an Electromyography Detection System for the Control of Functional Electrical Stimulation in Neurological Rehabilitation , 2011 .

[20]  M. Crepaldi,et al.  A quasi-digital radio system for muscle force transmission based on event-driven IR-UWB , 2012, 2012 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[21]  Guido Masera,et al.  An all-digital spike-based ultra-low-power IR-UWB dynamic average threshold crossing scheme for muscle force wireless transmission , 2015, 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[22]  Marco Crepaldi,et al.  A wireless address-event representation system for ATC-based multi-channel force wireless transmission , 2013, 5th IEEE International Workshop on Advances in Sensors and Interfaces IWASI.