Skip to main content

Vertical Hand Position Estimation with Wearable Differential Barometery Supported by RFID Synchronization

  • Conference paper
  • First Online:
Body Area Networks: Smart IoT and Big Data for Intelligent Health Management (BODYNETS 2019)

Abstract

We demonstrate how a combination of a wrist-worn and stationary barometer can be used to track the vertical position of the user’s Hand with an accuracy in the range of 30 cm. To this end, the two barometers synchronized each time an RFID reader detects them being in proximity of each other. The accuracy is sufficient to detect a specific shelve of a cupboard on which something has been placed or determine if the user’s hand is touching his/her head or the torso. The advantage of the method over IMU based approaches is that it requires only a wrist-worn sensor (as could be implemented in a smartwatch) and a reference either in an often access location in the environment or a pocket (e.g.in the smartphone) and it does not depend on a stable magnetic environment. The proposed system was tested in two different activities: Shelve recognition in a warehouse picking scenario and movement of the arm to specific body locations. Despite the simplicity of our method, it shows initial results between 55–62% and 73–91% accuracy, respectively.

The research reported in this paper has been partially supported by the BMBF (German Federal Ministry of Education and Research) in the project HeadSense(project number 01IW18001). The support is gratefully acknowledged.

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 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

Institutional subscriptions

References

  1. Koepp, R., Allen, T., Fassett, J., Teng, A.: Achieving high speed RFID die pick and place operation. In: 2008 33rd IEEE/CPMT International Electronics Manufacturing Technology Conference (IEMT), pp. 1–8 (2008)

    Google Scholar 

  2. Cho, W., Shin, M., Jang, J., Paik, J.: Robust pedestrian height estimation using principal component analysis and its application to automatic camera calibration. In: 2018 International Conference on Electronics, Information, and Communication, pp. 1–2 (2018)

    Google Scholar 

  3. Cola, G., Avvenuti, M., Piazza, P., Vecchio, A.: Fall Detection using a head-worn barometer. In: Perego, P., Andreoni, G., Rizzo, G. (eds.) MobiHealth 2016. LNICST, vol. 192, pp. 217–224. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58877-3_29

    Chapter  Google Scholar 

  4. Ehrlich, C.R., Blankenbach, J.: Pedestrian localisation inside buildings based on multi-sensor smartphones. In: 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS), pp. 1–10 (2018)

    Google Scholar 

  5. Fishkin, K.P., Philipose, M., Rea, A.: Hands-on RFID: wireless wearables for detecting use of objects. In: Ninth IEEE International Symposium on Wearable Computers (ISWC 2005), pp. 38–41 (2005)

    Google Scholar 

  6. Hou, X., Arslan, T.: Monte carlo localization algorithm for indoor positioning using bluetooth low energy devices. In: 2017 International Conference on Localization and GNSS (ICL-GNSS), pp. 1–6 (2017)

    Google Scholar 

  7. Lee, D., Park, K.W., Park, C., Kang, I.: An efficient heave estimation using time-differenced gps carrier phase measurements and compensated barometer measurement applying error model. In: 2015 International Association of Institutes of Navigation World Congress, pp. 1–6 (2015)

    Google Scholar 

  8. Ogris, G., Stiefmeier, T., Junker, H., Lukowicz, P., Troster, G.: Using ultrasonic hand tracking to augment motion analysis based recognition of manipulative gestures. In: Ninth IEEE International Symposium on Wearable Computers, pp. 152–159 (2005)

    Google Scholar 

  9. Parviainen, J., Kantola, J., Collin, J.: Differential barometry in personal navigation. In: 2008 IEEE/ION Position, Location and Navigation Symposium, pp. 148–152 (2008)

    Google Scholar 

  10. Riyadi, M.A., Sudira, N., Hanif, M.H., Triwiyatno, A.: Design of pick and place robot with identification and classification object based on RFID using stm32vldiscovery. In: 2017 International Conference on Electrical Engineering and Computer Science (ICECOS), pp. 171–176 (2017)

    Google Scholar 

  11. Rodríguez-Martín, D., Samà, A., Pérez-López, C., Català, A.: Posture transitions identification based on a triaxial accelerometer and a barometer sensor. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2017. LNCS, vol. 10306, pp. 333–343. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59147-6_29

    Chapter  Google Scholar 

  12. Wei, S., Dan, G., Chen, H.: Altitude data fusion utilising differential measurement and complementary filter. IET Sci. Measur. Technol. 10(8), 874–879 (2016)

    Article  Google Scholar 

  13. Woelfle, M., Guenthner, W.A.: Wearable RFID in order picking systems. In: RFID SysTech 2011 7th European Workshop on Smart Objects: Systems, Technologies and Applications, pp. 1–6 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hymalai Bello .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bello, H., Rodriguez, J., Lukowicz, P. (2019). Vertical Hand Position Estimation with Wearable Differential Barometery Supported by RFID Synchronization. In: Mucchi, L., Hämäläinen, M., Jayousi, S., Morosi, S. (eds) Body Area Networks: Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-030-34833-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-34833-5_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34832-8

  • Online ISBN: 978-3-030-34833-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics