Skip to main content
SpringerLink
Log in

Data Flow-Driven BAN: Architecture and Algorithms

  • Chapter
Modern Stroke Rehabilitation through e-Health-based Entertainment

  • 1514 Accesses

Abstract

This chapter reflects the objectives and the design of the StrokeBack Body Area Network (BAN) with respect to the requirements given by the application scenarios of clinical assessment and recognition of daily activities, as well as the user feedback from patients and therapists. The StrokeBack sensor platform, called GHOST, is designed to provide smaller size, more efficient power consumption, stronger security means and much better usability than the currently available state-of-the-art sensing devices. The GHOST platform is a tiny sensor platform with inertial sensors, Qi compliant wireless power transfer and an embedded Bluetooth Low-Energy (Bluetooth 4.0) front end. In a full package, the complete GHOST sensor platform is of matchbox size only, including a Lithium cell battery pack and a receiver coil for wireless power transfer. Two versions of the StrokeBack sensor nodes have been assembled. The first sensor node design uses the shelf components while the second variant of the sensor nodes incorporates a customised crypto-microcontroller providing enhanced security options. This platform is used for detection of Activities of Daily Living (ADL) within the StrokeBack project, where the patient wears the platform at the affected limb. The daily data flow principle is described in detail. The platform collects movement data from accelerometer, gyroscope and magnetometer throughout the day, which are uploaded and analysed during the night when the sensor resides in the wireless charging station.

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

Access this chapter

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. V.M. Pomeroy, E. Evans, J.D. Richards, Agreement between an electrogoniometer and motion analysis system measuring angular velocity of the knee during walking after stroke. Physiotherapy 92(3), 159–165 (2006)

    Article  Google Scholar 

  2. A. Vega-Gonzalez, M.H. Granat, Continuous monitoring of upper-limb activity in a free-living environment. Arch. Phys. Med. Rehabil. 86(3), 541–548 (2005)

    Article  Google Scholar 

  3. K. Aminian, B. Najafi, C. Bula, P.F. Leyvraz, P. Robert, Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. J. Biomech. 35(5), 689–699 (2002)

    Article  Google Scholar 

  4. E. Bernmark, C. Wiktorin, A triaxial accelerometer for measuring arm movements. Appl. Ergon. 33(6), 541–548 (2002)

    Article  Google Scholar 

  5. A.V. Rowlands, P.W.M. Thomas, R.G. Eston, R. Topping, Validation of the RT3 triaxial accelerometer for the assessment of physical activity. Med. Sci. Sports. Exerc. 36(3), 518–524 (2004)

    Article  Google Scholar 

  6. G. Uswatte, W.H. Miltner, B. Foo, M. Varma, S. Moran, E. Taub, Objective measurement of functional upper-extremity movement using accelerometer recordings transformed with a threshold filter. Stroke 31(3), 662–667 (2000)

    Article  Google Scholar 

  7. G. Uswatte, W.L. Foo, H. Olmstead, K. Lopez, A. Holland, L.B. Simms, Ambulatory monitoring of arm movement using accelerometry: an objective measure of upper-extremity rehabilitation in persons with chronic stroke. Arch. Phys. Med. Rehabil. 86(7), 1498–1501 (2005)

    Article  Google Scholar 

  8. T. Ebata, S. Iwasaki, R. Kamide, M. Niimura, Use of a wrist activity monitor for the measurement of nocturnal scratching in patients with atopic dermatitis. Br. J. Dermatol. 144(2), 305–309 (2001)

    Article  Google Scholar 

  9. C. Fairfield-Estill, L.A. MacDonald, T.B. Wenzl, M.R. Peterson, Use of accelerometers as an ergonomic assessment method for arm acceleration: a large-scale field trial. Ergonomics 43(9), 1430–1445 (2000)

    Article  Google Scholar 

  10. H. Kumahara, H. Tanaka, Y. Schutz, Daily physical activity assessment: what is the importance of upper limb movements vs. whole body movements? Int. J. Obes. Relat. Metab. Disord. 28(9), 1105–1110 (2004)

    Article  Google Scholar 

  11. J. Levine, E. Melanson, K. Westerterp, J. Hill, Tracmor system for measuring walking energy expenditure. Eur. J. Clin. Nutr. 57(9), 1176–1180 (2003)

    Article  Google Scholar 

  12. R. Riener, M. Ferrarin, E. Pavan, C. Frigo, Patient-driven control of FES-supported standing up and sitting down: experimental results. IEEE Trans. Rehabil. Eng. 8(4), 523–529 (2000)

    Article  Google Scholar 

  13. K. Tong, M. Granat, Virtual artificial sensor technique for functional electrical stimulation. Med. Eng. Phys. 20(60), 458–468 (1998)

    Article  Google Scholar 

  14. P. Veltink, P. Slycke, J. Hemssems, R. Buschman, H. Hermens, Three-dimensional inertial sensing of foot movements for automatic tuning of a two-channel implantable drop-foot stimulator. Med. Eng. Phys. 25(1), 21–28 (2003)

    Article  Google Scholar 

  15. M. Kutlu, CT. Freeman, E. Hallewell, A.-M. Hughes, D.S. Laila, Electrode-array based functional electrical stimulation for upper-limb stroke rehabilitation with innovative sensing and control, in Technically Assisted Rehabilitation Conference (TAR 2015), Berlin, 2015

    Google Scholar 

  16. P. Gibbs, H. Asada, Wearable conductive fiber sensors for multi-axis human joint angle measurements, J. Neuroeng. Rehabil. 2(7), 2005

    Google Scholar 

  17. M. Munih, M. Ichie, Current status and future prospects for upper and lower extremity motor system neuroprostheses. Neuromodulation 4(4), 176–186 (2001)

    Article  Google Scholar 

  18. Z. Rong, Z. Zhaoying, A real-time articulated human motion tracking system using tri-axis inertial/magnetic sensors package. IEEE Trans. Neural Syst. Rehabil. Eng. 12(2), 295–302 (2004)

    Article  Google Scholar 

  19. H. Zhou, H. Hu, Inertial sensors for motion detection of human upper limbs. Sens. Rev. 27(2), 151–158 (2007)

    Article  Google Scholar 

  20. SenseWear, http://sensewear.bodymedia.com. Accessed 15 Apr 2015

  21. Samsung Gear Fit, http://www.samsung.com/de/consumer/mobile-device/wearable/wearable/SM-R3500ZKADBT. Accessed 15 Apr 2015

  22. FitnessShirt, http://www.iis.fraunhofer.de/de/ff/med/prod/sensorik/fitnessshirt.html. Accessed 15 Apr 2015

  23. P. Bonato, Wearable sensors/systems and their impact on biomedical engineering. IEEE Eng. Med. Biol. Mag. 22(3), 18–20 (2003)

    Article  Google Scholar 

  24. SMART project, http://thesmartconsortium.org/

  25. A. Tognetti, F. Lorussi, R. Bartalesi, S. Quaglini, M. Tesconi, G. Zupone, Wearable kinesthetic system for capturing and classifying upper limb gesture in post-stroke rehabilitation, J. Neuroeng. Rehabil. 2(8), 2005

    Google Scholar 

  26. T.M. Seeberg, J. Vedum, M. Sandsund, H.O. Austad, A.E. Liverud, A.-S.B. Vardøy, I. Svagård, F. Strisland, Development of a wearable multisensor device enabling continuous monitoring of vital signs and activity, in IEEE-EMBS International Conference on Biomedical and Health Informatics, Valencia, 2014

    Google Scholar 

  27. F. Peter, L. Landgraf, T. Seel, T. Schauer, Using inertial measurement units for measuring body segment orientations in indoor environments, in Technically Assisted Rehabilitation Conference (TAR 2015), Berlin, 2015

    Google Scholar 

  28. M. Kok, J.D. Hol, T.B. Schön, An optimization-based approach to human body motion capture using inertial sensors, in Proceedings of the 19th World Congress of the International Federation of Automatic Control, Cape Town, 2014, pp. 79–85

    Google Scholar 

  29. A.E. Liverud, J. Vedum, F. Fleurey, T.M. Seeberg, Wearable wireless multi-parameter sensor module for physiological monitoring. Stud. Health Technol. Inform. 177, 210–215 (2012)

    Google Scholar 

  30. I. Tarkka, A. Remes, Improving disabled arm control through gaming, in Technically Assisted Rehabilitation Conference (TAR 2015), Berlin, 2015

    Google Scholar 

  31. H. Weber, Technically assisted rehabilitation of the hand after stroke by means of the HFD 200 hand and finger dynamometer, in Technically Assisted Rehabilitation Conference (TAR 2015), Berlin, 2015

    Google Scholar 

  32. D. Biswas, A. Cranny, N. Gupta, K. Maharatna, J. Achner, J. Klemke, M. Jöbges, S. Ortmann, Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification. Hum. Mov. Sci. 40, 59–76 (2015). doi:10.1016/j.humov.2014.11.013

    Article  Google Scholar 

  33. D. Biswas, A. Cranny, A.F. Rahim, N. Gupta, N. R. Harris, K. Maharatna, S. Ortmann, On the data analysis for classification of elementary upper limb movements, Biomed. Eng. Lett. 4(4), 403–413 (2014). ISSN: 2093-9868 (print) 2093-985X (online), doi: 10.1007/s13534-014-0160-0

    Google Scholar 

  34. Shimmer, www.shimmersensing.com. Accessed 15 Apr 2015

  35. Xsens Technologies B.V., www.xsens.com. Accessed 15 Apr 2015

  36. Wireless power Consortium (15/4/2015), http://www.wirelesspowerconsortium.com. Accessed 15 Apr 2015

  37. Odenwälder Kunststoffwerke Gehäusesysteme GmbH, http://www.okw.de. Accessed 15 Apr 2015

  38. Griffin Technology: slap for iPod nano, http://store.griffintechnology.com/slap. Accessed 15 Apr 2015

Download references

Author information

Authors and Affiliations

  1. IHP, Frankfurt, Germany

    Steffen Ortmann & Peter Langendörfer

Authors
  1. Steffen Ortmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter Langendörfer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Steffen Ortmann .

Editor information

Editors and Affiliations

  1. (RFSAT) Ltd, Research for Science, Art and Technology, Sheffield, United Kingdom

    Emmanouela Vogiatzaki

  2. Intracom S. A. Telecom Solutions, Peania, Greece

    Artur Krukowski

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Ortmann, S., Langendörfer, P. (2016). Data Flow-Driven BAN: Architecture and Algorithms. In: Vogiatzaki, E., Krukowski, A. (eds) Modern Stroke Rehabilitation through e-Health-based Entertainment. Springer, Cham. https://doi.org/10.1007/978-3-319-21293-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-21293-7_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21292-0

  • Online ISBN: 978-3-319-21293-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation