Skip to main content

Improvement of Body Posture Changes Detection During Ambulatory Respiratory Measurements Using Impedance Pneumography Signals

  • Conference paper
  • First Online:
XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016

Part of the book series: IFMBE Proceedings ((IFMBE,volume 57))

Abstract

Impedance pneumography could be used for measuring respiratory parameters quantitatively in ambulatory conditions. It was noted that body posture affects the calibration coefficient connecting the measured impedance values and their first derivatives with volume and flow reference signals. Standard techniques for automatic detection of body posture and activity usually require additional motion sensors. However, in terms of the measurement comfort, less number of sensors is needed. Single sensor mounted on the chest provides good results, however its accuracy decreases during frequent changes of body posture. The aim of this study was to assess the possibility to detect body posture changes using the impedance signal itself, without any other devices or the active cooperation of the person being studied and prospectively improving the body posture change detection method using single motion sensor (e.g. 3D accelerometer). Fifteen healthy students (11 males) performed two body posture changes - get-ups and stand-ups. Six classification techniques were checked for prediction accuracy. It was found that artificial neural networks provided the best overall accuracy (90%).

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ghafar-Zadeh E (2015) Wireless Integrated Biosensors for Point-of-Care Diagnostic Applications. Sensors 15/2:3236-3261

    Google Scholar 

  2. Yao Q, Tian Y, Li PF, Tian LL, Qian YM, Li YS (2015) Design and Development of a Medical Big Data Processing System Based on Ha-doop. Journal of Medical Systems 39/23/3

    Google Scholar 

  3. Seppa VP, Viik J, Hyttinen J (2010) Assessment of Pulmonary Flow Using Impedance Pneumography. IEEE Transactions on Biomedical Engineering 57/9:2277–2285

    Google Scholar 

  4. Młyńczak M, Niewiadomski W, Żyliński M, Cybulski G (2015) Verification of the Respiratory Parameters Derived from Impedance Pneumography during Normal and Deep Breathing in Three Body Postures. MBEC IFMBE Proceedings 45:881-884

    Google Scholar 

  5. Młyńczak M, Niewiadomski W, Żyliński M, Cybulski G (2015) Ability to Determine Dynamic Respiratory Parameters Evaluated during Forced Vital Capacity Maneuver Using Impedance Pneumography. MBEC IFMBE Proceedings 45:877-880

    Google Scholar 

  6. Houtveen JH, Groot PFC, de Geus EJC (2006) Validation of the thoracic impedance derived respiratory signal using multilevel analysis. Elsevier 59:97-106

    Google Scholar 

  7. Foerster F, Smeja M, Fahrenberg J (1999) Detection of posture and motion by accelerometry: A validation study in ambulatory monitoring. Computers in Human Behavior 15:571-583

    Google Scholar 

  8. Tapia E, Intille S, Larson K (2004) Activity recognition in the home using simple and ubiquitous sensors. Proc. of 2nd International Conference on Pervasive Computing 158-175

    Google Scholar 

  9. Busser HJ, Ott J, van Lummel RC, Uiterwaal M, Blank R (1997) Ambulatory monitoring of children’s activity. Medical Engineering & Physics 19/5:440-445

    Google Scholar 

  10. Bouten C, Sauren A, Verduin M, Janssen J (1997) Effects of placement and orientation of body-fixed accelerometers on the assessment of energy expenditure during walking. Medical & Biological Engineering & Computing 35:50-56

    Google Scholar 

  11. Fruin M, Rankin J (2004) Validity of a multi-sensor armband in estimating rest and exercise energy expenditure. Medicine & Science in Sports & Exercise 36:1063-1069

    Google Scholar 

  12. Bouten CVC, Koekkoek KTM, Verduin M, Kodde R, Janssen JD (1997) A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Transactions on Biomedical Engineering 44:136-147

    Google Scholar 

  13. Ermes M, Parkka J, Mantyjarvi J, Korhonen I (2008) Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions. IEEE Transactions on Information Tech- nology in Biomedicine 12/1:20-26

    Google Scholar 

  14. Dargent-Molina P, Favier F, Grandjean H, Baudoin C, Schott A, Hausherr E, Meunier P, Breart G (1996) Fall-related factors and risk of hip fracture: The EPIDOS prospective study. Lancet 348:145-149

    Google Scholar 

  15. Godfrey A, Conway R, Meagher D, OLaighin G (2008) Direct measurement of human movement by accelerometry, Medical Engineering & Physics 30:1364-1386

    Google Scholar 

  16. Zhang Z (2012) Microsoft Kinect Sensor and Its Effect. IEEE Multi Media 19/2:4-10

    Google Scholar 

  17. Sitnik S, Witkowski M (2008) Locating and tracing of anatomical landmarks based on full-field four-dimensional measurement of human body surface. Journal of Biomedical Optics 13/4:044039-1-044039-11

    Google Scholar 

  18. Karantonis DM, Narayanan MR, Mathie M, Lovell N, Celler B (2006) Implementation of a Real-Time Human Movement Classifier Using a Triaxial Accelerometer for Ambulatory Monitoring. IEEE Transactions on Information Technology in Biomedicine 10/1:156-167

    Google Scholar 

  19. Mathie MJ, Coster ACF, Lovell NH, Celler BG (2003) Detection of daily physical activities using a triaxial accelerometer. Medical & Biological Engineering & Computing 41:296-301

    Google Scholar 

  20. Fahrenberg J, Foerster F, Smeja M, Mueller W (1997) Assessment of posture and motion by multichannel piezoresistive accelerometer recordings. Psychophysiology 34:607-612

    Google Scholar 

  21. Balogun JA, Amusa LO, Onyewadume IU (1988) Factors affecting caltrac and calcount accelerometers output. Physical Therapy 68:1500-1504

    Google Scholar 

  22. Hagan MT, Menhaj MB (1994) Training Feedforward Networks with the Marquardt Algorithm. IEEE Transactions on Neural Networks 5/6:989-993

    Google Scholar 

  23. Meyer D, Dimitriadou E, Hornik K, Weingessel A, Leisch F, Chang CC, Lin CC (2014) e1071: Misc Functions of the Department of Statistics. URL http://cran.r-project.org/web/packages/e1071/index.html

  24. Therneau T, Atkinson B, Ripley B (2015) rpart: Recursive Partitioning and Regression Trees. URL http://cran.rproject.org/web/packages/rpart/index.html

  25. Ridgeway G (2003) gbm: Generalized Boosted Regression Models. URL http://CRAN.R-project.org/package=gbm

  26. Liaw A, Wiener M (2002) Classification and Regression by randomForest. R News 2/3:18-22

    Google Scholar 

  27. Kuhn M (2008) Building Predictive Models in R Using the caret Package. Journal of Statistical Software, vol. 28, Issue 5, 2008.

    Google Scholar 

  28. Młyńczak M, Niewiadomski W, Żyliński M, Cybulski G (2014) Ambulatory impedance pneumography device for quantitative monitoring of volumetric parameters in respiratory and cardiac applications. Computing in Cardiology 41:965-968

    Google Scholar 

  29. Seppa VP, Hyttinen J, Uitto M, Chrapek W, Viik J (2013) Novel electrode configuration for highly linear impedance pneumography. Biomed Tech 58/1:35-38

    Google Scholar 

  30. Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27/8:1226-1238

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marcel Młyńczak .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Młyńczak, M., Cybulski, G. (2016). Improvement of Body Posture Changes Detection During Ambulatory Respiratory Measurements Using Impedance Pneumography Signals. In: Kyriacou, E., Christofides, S., Pattichis, C. (eds) XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-319-32703-7_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-32703-7_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32701-3

  • Online ISBN: 978-3-319-32703-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics