Skip to main content
SpringerLink
Log in
Book cover

International Conference on Bioinformatics and Biomedical Engineering

IWBBIO 2017: Bioinformatics and Biomedical Engineering pp 608–620Cite as

A Portable Wireless sEMG and Inertial Acquisition System for Human Activity Monitoring

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10209))

Abstract

This paper presents a low-cost portable wireless system specifically designed to acquire both surface electromyography (sEMG) and accelerometer signals for healthcare applications, sport, and fitness activities. The system, consists of several ultralight wireless sensing nodes that acquire, amplify, digitize, and transmit the sEMG and accelerometer signals to one or more base stations through a 2.4 GHz radio link using a custom-made communication protocol designed on top of the IEEE 802.15.4 physical layer. Additionally, the system can be easily configured to capture and process many other biological signals such as the electrocardiographic (ECG) signal. Each base station is connected through a USB link to a control PC running a user interface software for viewing, recording, and analysing the data.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Biagetti, G., Crippa, P., Curzi, A., Orcioni, S., Turchetti, C.: A multi-class ECG beat classifier based on the truncated KLT representation. In: 2014 European Modelling Symposium, pp. 93–98, October 2014

    Google Scholar 

  2. Crippa, P., Curzi, A., Falaschetti, L., Turchetti, C.: Multi-class ECG beat classification based on a Gaussian mixture model of Karhunen-Loève transform. Int. J. Simul. Syst. Sci. Technol. 16(1), 2.1–2.10 (2015)

    Google Scholar 

  3. Bacà, A., Biagetti, G., Camilletti, M., Crippa, P., Falaschetti, L., Orcioni, S., Rossini, L., Tonelli, D., Turchetti, C.: CARMA: a robust motion artifact reduction algorithm for heart rate monitoring from PPG signals. In: 23rd European Signal Processing Conference, pp. 2696–2700, September 2015

    Google Scholar 

  4. Biagetti, G., Crippa, P., Curzi, A., Orcioni, S., Turchetti, C.: Analysis of the EMG signal during cyclic movements using multicomponent AM-FM decomposition. IEEE J. Biomed. Health Inform. 19(5), 1672–1681 (2015)

    Article  MATH  Google Scholar 

  5. Biagetti, G., Crippa, P., Falaschetti, L., Orcioni, S., Turchetti, C.: An efficient technique for real-time human activity classification using accelerometer data. In: Czarnowski, I., Caballero, A.M., Howlett, R.J., Jain, L.C. (eds.) Intelligent Decision Technologies 2016. Smart Innovation, Systems and Technologies, vol. 56, pp. 425–434. Springer, Cham (2016)

    Google Scholar 

  6. Rodriguez-Martin, D., Samà, A., Perez-Lopez, C., Català, A., Cabestany, J., Rodriguez-Molinero, A.: SVM-based posture identification with a single waist-located triaxial accelerometer. Expert Syst. Appl. 40(18), 7203–7211 (2013)

    Article  Google Scholar 

  7. Mannini, A., Intille, S.S., Rosenberger, M., Sabatini, A.M., Haskell, W.: Activity recognition using a single accelerometer placed at the wrist or ankle. Med. Sci. Sports Exerc. 45(11), 2193–2203 (2013)

    Article  Google Scholar 

  8. Torres-Huitzil, C., Nuno-Maganda, M.: Robust smartphone-based human activity recognition using a tri-axial accelerometer. In: 2015 IEEE 6th Latin American Symposium on Circuits Systems, pp. 1–4, February 2015

    Google Scholar 

  9. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Energy efficient smartphone-based activity recognition using fixed-point arithmetic. J. Univ. Comput. Sci. 19(9), 1295–1314 (2013)

    Google Scholar 

  10. Dernbach, S., Das, B., Krishnan, N.C., Thomas, B.L., Cook, D.J.: Simple and complex activity recognition through smart phones. In: 8th International Conference on Intelligent Environments, pp. 214–221, June 2012

    Google Scholar 

  11. Khan, A., Lee, Y.K., Lee, S., Kim, T.S.: Human activity recognition via an accelerometer-enabled-smartphone using kernel discriminant analysis. In: 2010 5th International Conference on Future Information Technology, pp. 1–6, May 2010

    Google Scholar 

  12. Mannini, A., Sabatini, A.M.: Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10(2), 1154–1175 (2010)

    Article  Google Scholar 

  13. Catal, C., Tufekci, S., Pirmit, E., Kocabag, G.: On the use of ensemble of classifiers for accelerometer-based activity recognition. Appl. Soft Comput. 37, 1018–1022 (2015)

    Article  Google Scholar 

  14. Biagetti, G., Crippa, P., Falaschetti, L., Orcioni, S., Turchetti, C.: Artifact reduction in photoplethysmography using Bayesian classification for physical exercise identification. In: Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods, Rome, Italy, pp. 467–474, February 2016

    Google Scholar 

  15. Biagetti, G., Crippa, P., Falaschetti, L., Orcioni, S., Turchetti, C.: A rule based framework for smart training using sEMG signal. In: Neves-Silva, R., Jain, L.C., Howlett, R.J. (eds.) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol. 39, pp. 89–99. Springer, Heidelberg (2015)

    Google Scholar 

  16. Lee, S.Y., Koo, K.H., Lee, Y., Lee, J.H., Kim, J.H.: Spatiotemporal analysis of EMG signals for muscle rehabilitation monitoring system. In: 2013 IEEE 2nd Global Conference on Consumer Electronics, pp. 1–2, October 2013

    Google Scholar 

  17. Chang, K.M., Liu, S.H., Wu, X.H.: A wireless sEMG recording system and its application to muscle fatigue detection. Sensors 12(1), 489–499 (2012)

    Article  Google Scholar 

  18. Pantelopoulos, A., Bourbakis, N.: A survey on wearable biosensor systems for health monitoring. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4887–4890, August 2008

    Google Scholar 

  19. Biagetti, G., Crippa, P., Orcioni, S., Turchetti, C.: Homomorphic deconvolution for MUAP estimation from surface EMG signals. IEEE J. Biomed. Health Inform. 21(2), 328–338 (2017)

    Article  Google Scholar 

  20. Biagetti, G., Crippa, P., Orcioni, S., Turchetti, C.: Surface EMG fatigue analysis by means of homomorphic deconvolution. In: Conti, M., Madrid, N.M., Seepold, R., Orcioni, S. (eds.) Mobile Networks for Biometric Data Analysis. Lecture Notes in Electrical Engineering, vol. 392, pp. 173–188. Springer, Heidelberg (2016)

    Chapter  Google Scholar 

  21. Biagetti, G., Crippa, P., Falaschetti, L., Orcioni, S., Turchetti, C.: Wireless surface electromyograph and electrocardiograph system on 802.15.4. IEEE Trans. Consum. Electron. 62(3), 258–266 (2016)

    Article  Google Scholar 

  22. Nawab, S.H., Roy, S.H., Luca, C.J.D.: Functional activity monitoring from wearable sensor data. In: The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, pp. 979–982, September 2004

    Google Scholar 

  23. Spulber, I., Georgiou, P., Eftekhar, A., Toumazou, C., Duffell, L., Bergmann, J., McGregor, A., Mehta, T., Hernandez, M., Burdett, A.: Frequency analysis of wireless accelerometer and EMG sensors data: towards discrimination of normal and asymmetric walking pattern. In: 2012 IEEE International Symposium on Circuits and Systems, pp. 2645–2648, May 2012

    Google Scholar 

  24. Ghasemzadeh, H., Jafari, R., Prabhakaran, B.: A body sensor network with electromyogram and inertial sensors: multimodal interpretation of muscular activities. IEEE Trans. Inf. Technol. Biomed. 14(2), 198–206 (2010)

    Article  Google Scholar 

  25. Roy, S.H., Cheng, M.S., Chang, S.S., Moore, J., Luca, G.D., Nawab, S.H., Luca, C.J.D.: A combined sEMG and accelerometer system for monitoring functional activity in stroke. IEEE Trans. Neural Syst. Rehabil. Eng. 17(6), 585–594 (2009)

    Article  Google Scholar 

  26. Wang, Q., Chen, X., Chen, R., Chen, Y., Zhang, X.: Electromyography-based locomotion pattern recognition and personal positioning toward improved context-awareness applications. IEEE Trans. Syst. Man Cybern. Syst. 43(5), 1216–1227 (2013)

    Article  Google Scholar 

  27. Zhang, X., Chen, X., Li, Y., Lantz, V., Wang, K., Yang, J.: A framework for hand gesture recognition based on accelerometer and EMG sensors. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 41(6), 1064–1076 (2011)

    Article  Google Scholar 

  28. Biagetti, G., Crippa, P., Orcioni, S., Turchetti, C.: An analog front-end for combined EMG/ECG wireless sensors. In: Conti, M., Madrid, N.M., Seepold, R., Orcioni, S. (eds.) Mobile Networks for Biometric Data Analysis. Lecture Notes in Electrical Engineering, vol. 392, pp. 215–224. Springer, Heidelberg (2016)

    Chapter  Google Scholar 

  29. van Beelen, T.: EDFbrowser. http://www.teuniz.net/edfbrowser/. Accessed 30 June 2014

  30. Hermens, H.J., Freriks, B.: European recommendations for surface electromyography [CDROM]. Roessingh Research and Development (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DII – Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131, Ancona, Italy

    Giorgio Biagetti, Paolo Crippa, Laura Falaschetti, Simone Orcioni & Claudio Turchetti

Authors
  1. Giorgio Biagetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paolo Crippa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laura Falaschetti
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Simone Orcioni
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Claudio Turchetti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paolo Crippa .

Editor information

Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Ignacio Rojas

  2. Universidad de Granada, Granada, Spain

    Francisco Ortuño

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Biagetti, G., Crippa, P., Falaschetti, L., Orcioni, S., Turchetti, C. (2017). A Portable Wireless sEMG and Inertial Acquisition System for Human Activity Monitoring. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10209. Springer, Cham. https://doi.org/10.1007/978-3-319-56154-7_54

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-56154-7_54

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56153-0

  • Online ISBN: 978-3-319-56154-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation