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Wireless Low Energy System Architecture for Event-Driven Surface Electromyography

  • Fabio RossiEmail author
  • Paolo Motto Ros
  • Stefano Sapienza
  • Paolo Bonato
  • Emilio Bizzi
  • Danilo Demarchi
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 573)

Abstract

The development of surface ElectroMyoGraphy (sEMG) acquisition system having an optimal trade-off between accuracy, resolution, low dimension and power consumption is a hot topic today. The event-driven Average Threshold Crossing (ATC) technique applied to the sEMG signal allows the reduction of both complexity and power consumption of the acquisition board. The paper presents an sEMG acquisition system, based on this approach, and shows the advantages of the ATC in this field. A framework for developing bio-signal ATC-processing applications is provided, enabling the comparison with a standard sEMG sampling approach. Both system performance and power consumption analyses are carried out to obtain promising results in terms of real-time behavior and energy saving. As a sample application, the system is employed in the control of Functional Electrical Stimulation (FES) in way to verify the behavior of the ATC approach in such application.

Keywords

Surface ElectroMyoGraphy Event-driven Average threshold crossing Bluetooth low energy Functional electrical stimulation 

References

  1. 1.
    Robertson, D.G.E.: Research Methods in Biomechanics. 2 edn, part III, chapter 8: Electromyographic Kinesiology - Gary Kamen (2004)Google Scholar
  2. 2.
    Mitra, S., Acharya, T.: Gesture recognition: a survey. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 37, 311–324 (2007)CrossRefGoogle Scholar
  3. 3.
    Chang, Y.-J., Chen, S.-F., Huang, J.-D.: A Kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities, vol. 32, pp. 2566–2570 (2011)Google Scholar
  4. 4.
    Girolamo, M.D., Favetto, A., Paleari, M., Celadon, N., Ariano, P.: A comparison of sEMG temporal and spatial information in the analysis of continuous movements. Inf. Med. Unlocked 9, 255–263 (2017)CrossRefGoogle Scholar
  5. 5.
    Atzori, M., Gijsberts, A., Kuzborskij, I., Elsig, S., Mittaz Hager, A.-G., Deriaz, O., Castellini, C., Müller, H., Caputo, B.: Characterization of a Benchmark Database for Myoelectric Movement Classification (2015)Google Scholar
  6. 6.
    Jani, A.B., Bagree, R., Roy, A.K.: Design of a low-power, low-cost ECG EMG sensor for wearable biometric and medical application. In: 2017 IEEE Sensors, pp. 1–3, Oct 2017Google Scholar
  7. 7.
    Crepaldi, M., Paleari, M., Bonanno, A., Sanginario, A., Ariano, P., Tran, D.H., Demarchi, D.: A quasi-digital radio system for muscle force transmission based on event-driven IR-UWB. In: 2012 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 116–119, Nov 2012Google Scholar
  8. 8.
    Motto Ros, P., Paleari, M., Celadon, N., Sanginario, A., Bonanno, A., Crepaldi, M., Ariano, P., Demarchi, D.: A wireless address-event representation system for ATC-based multi-channel force wireless transmission. In: 5th IEEE International Workshop on Advances in Sensors and Interfaces IWASI, pp. 51–56, June 2013Google Scholar
  9. 9.
    Sapienza, S., Crepaldi, M., Motto Ros, P., Bonanno, A., Demarchi, D.: On integration and validation of a very low complexity ATC UWB system for muscle force transmission. IEEE Trans. Biomed. Circuits Syst. 10, 497–506, April 2016CrossRefGoogle Scholar
  10. 10.
    Shalaby, R.E.-S.: Development of an electromyography detection system for the control of functional electrical stimulation in neurological rehabilitation (2011)Google Scholar
  11. 11.
    Bitalino: BITalino Board Kit Data Sheet (2015)Google Scholar
  12. 12.
    BTS Bioengineering: BTS FreeEMG user manual, October 2008Google Scholar
  13. 13.
  14. 14.
    Guzman, D.A.F., Sapienza, S., Sereni, B., Motto Ros, P.: Very low power event-based surface EMG acquisition system with off-the-shelf components. In: IEEE Biomedical Circuits and Systems Conference (BioCAS) (2017).  https://doi.org/10.1109/BIOCAS.2017.8325152
  15. 15.
    Lichtman, A., Fuchs, P.: Hardware and software design for one channel ECG measurement using MSP430 microcontroller. In: 2018 Cybernetics Informatics (K I), pp. 1–5, Jan 2018Google Scholar
  16. 16.
    Bluetooth Special Interest Group (SIG) and Bluetooth SIG Working Groups, BLUETOOTH SPECIFICATION Version 4.0, June 2010Google Scholar
  17. 17.
    Microchip, RN4020 Bluetooth Low Energy Module DataSheet, September 2015Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Fabio Rossi
    • 1
    Email author
  • Paolo Motto Ros
    • 2
  • Stefano Sapienza
    • 3
  • Paolo Bonato
    • 3
  • Emilio Bizzi
    • 4
  • Danilo Demarchi
    • 1
  1. 1.Dipartimento di Elettronica e TelecomunicazioniPolitecnico di TorinoTurinItaly
  2. 2.Electronic Design LaboratoryIstituto Italiano di TecnologiaGenoaItaly
  3. 3.Department of Physical Medicine and RehabilitationHarvard Medical SchoolBostonUSA
  4. 4.Department of Brain and Cognitive SciencesMassachusetts Institute of TechnologyCambridgeUSA

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